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- Types of Variables in Research & Statistics | Examples
Types of Variables in Research & Statistics | Examples
Published on September 19, 2022 by Rebecca Bevans . Revised on December 2, 2022.
In statistical research , a variable is defined as an attribute of an object of study. Choosing which variables to measure is central to good experimental design .
If you want to test whether some plant species are more salt-tolerant than others, some key variables you might measure include the amount of salt you add to the water, the species of plants being studied, and variables related to plant health like growth and wilting .
You need to know which types of variables you are working with in order to choose appropriate statistical tests and interpret the results of your study.
You can usually identify the type of variable by asking two questions:
- What type of data does the variable contain?
- What part of the experiment does the variable represent?
Table of contents
Types of data: quantitative vs categorical variables, parts of the experiment: independent vs dependent variables, other common types of variables, frequently asked questions about variables.
Data is a specific measurement of a variable – it is the value you record in your data sheet. Data is generally divided into two categories:
- Quantitative data represents amounts
- Categorical data represents groupings
A variable that contains quantitative data is a quantitative variable ; a variable that contains categorical data is a categorical variable . Each of these types of variables can be broken down into further types.
Quantitative variables
When you collect quantitative data, the numbers you record represent real amounts that can be added, subtracted, divided, etc. There are two types of quantitative variables: discrete and continuous .
Categorical variables
Categorical variables represent groupings of some kind. They are sometimes recorded as numbers, but the numbers represent categories rather than actual amounts of things.
There are three types of categorical variables: binary , nominal , and ordinal variables .
*Note that sometimes a variable can work as more than one type! An ordinal variable can also be used as a quantitative variable if the scale is numeric and doesn’t need to be kept as discrete integers. For example, star ratings on product reviews are ordinal (1 to 5 stars), but the average star rating is quantitative.
Example data sheet
To keep track of your salt-tolerance experiment, you make a data sheet where you record information about the variables in the experiment, like salt addition and plant health.
To gather information about plant responses over time, you can fill out the same data sheet every few days until the end of the experiment. This example sheet is color-coded according to the type of variable: nominal , continuous , ordinal , and binary .
Experiments are usually designed to find out what effect one variable has on another – in our example, the effect of salt addition on plant growth.
You manipulate the independent variable (the one you think might be the cause ) and then measure the dependent variable (the one you think might be the effect ) to find out what this effect might be.
You will probably also have variables that you hold constant ( control variables ) in order to focus on your experimental treatment.
In this experiment, we have one independent and three dependent variables.
The other variables in the sheet can’t be classified as independent or dependent, but they do contain data that you will need in order to interpret your dependent and independent variables.
What about correlational research?
When you do correlational research , the terms “dependent” and “independent” don’t apply, because you are not trying to establish a cause and effect relationship ( causation ).
However, there might be cases where one variable clearly precedes the other (for example, rainfall leads to mud, rather than the other way around). In these cases you may call the preceding variable (i.e., the rainfall) the predictor variable and the following variable (i.e. the mud) the outcome variable .
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Once you have defined your independent and dependent variables and determined whether they are categorical or quantitative, you will be able to choose the correct statistical test .
But there are many other ways of describing variables that help with interpreting your results. Some useful types of variables are listed below.
You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .
In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:
- The independent variable is the amount of nutrients added to the crop field.
- The dependent variable is the biomass of the crops at harvest time.
Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .
A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.
A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.
In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.
Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).
Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).
You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .
Discrete and continuous variables are two types of quantitative variables :
- Discrete variables represent counts (e.g. the number of objects in a collection).
- Continuous variables represent measurable amounts (e.g. water volume or weight).
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- Statistics Research Paper
Statistics Research Paper Writing Guide + Examples
A statistics research paper discusses and analyzes the numerical data. A statistics research paper should cover all the aspects regarding the distribution of data, frequency tables and graphs..
A statistics research paper is similar to a survey research paper in many ways. Both papers focus on collecting information about some specific topic using surveys. They both use statistical methods to collect, analyze and present this information..
To explain how they are different, consider two types of statistics: means and relationships; it may be easier to understand this by first explaining how they’re related: The mean is (most often) calculated through addition while relationships are typically found through multiplication. This also explains why you calculate means at the beginning of a statistics project (before any relationship has been discovered), while responsibility for calculating relationships typically falls to the end of a statistics project. The next time you’re in class, try to count how many times your instructor mentions “mean,” as opposed to “relationship!”
A statistics paper is based on a relationship between two or more variables (often referred to as independent and dependent variables). You can think of these variables almost like social security numbers—each person has one SSN that distinguishes them from all other people; similarly, each data point (a unique combination of values for an independent variable) has its own set of values for one or more variables..
An example: if we are interested in knowing whether there is a relationship between hospital beds per 1,000 residents and the reading scores of senior citizens, defined as ages 65 and over, in a given city or country, then we must gather data on both variables. We will need to find out how many hospital beds there are per 1,000 residents for each of the cities or countries represented in our sample..
This type of research paper writing can also be based only on means (instead of relationships). Consider two more examples: If a psychologist wants to see if age is related to memory loss, but doesn’t care whether this relationship is positive or negative (i.e., she simply wants to know if older people tend to have better memories than younger people), all she cares about are the mean memory scores for groups defined by age.
On the other hand, if the psychologist wants to know if older people tend to have better memories than younger, but she also cares about how large this difference is (e.g.. their mean score is 10 points higher), then her research project will include calculations of relationships between age and memory scores.
How to write a statistics research paper
Getting started with your research paper is a difficult task. All you need to do is hop online and search for some information on how to write it but what are the necessary steps?
You can learn them by following this short guide:
Start by proper research:
Write an introduction, transition into your thesis statement, and finish off with a conclusion of sorts – this structure will not only help you produce a strong foundation, but also provide the reader with clues regarding where you’re going in particular when writing statistics essay.
The introduction should be broad enough to capture attention of the reader and yet narrow enough to indicate that it’s about something specific.
It can also serve as a springboard for later argumentation or present the central idea.
What makes a good research paper introduction , however, is a little bit of mystery or enigma that makes the reader go “I want to know more about this, why do they think so?”
Finding journal articles on statistics research paper
The next step is to locate journals and magazines. It’s best if you have some knowledge about what kind of scholarly work this will entail – statistics papers are not often found in tabloids but rather in peer reviewed sources.
If you’re struggling with compiling a comprehensive essay list of online sources, try asking for help at your school’s librarian or library.
There are also other options such as using one of those article databases which contain articles from all over the world and sorted by category: perhaps there’s something there – it doesn’t even have to be a journal article.
There are also books out there that have statistics papers in them – in case you want to go a little bit old school.
Writing body paragraphs
The next step is writing your body or main part of the statistics research paper, so it makes sense first to decide on what kind of statistical test you’ll use and then look up relevant information about it.
Use this information as the building blocks for paragraphs going into details: why did the statistician choose it?
What are some common criticisms/counterpoints?
Which should be supported by specific examples and further justification of why they’re relevant. The next section will deal with interpreting numerical results and drawing conclusions – this involves taking those numbers and making something meaningful out of them beyond just comparing them to each other. It could be a lot to take in, so it’s best if you break this down and look up specific information for different parts of it.
Write your paper
Now that you have collected all the necessary material, statistics research paper writing shouldn’t be hard – remember not to simply copy/paste the information from somewhere else without citing your sources and making sure that your work is better than what you’ve copied.
The final step will involve polishing and proofreading your work – make sure there are no mistakes when submitting or publishing online.
You should always use correct grammar, spelling and punctuation as well as consistent referencing/citation style (MLA, APA etc.).
And there you have it: statistics research paper.
Write perfect conclusion
The next step is writing your conclusions. You’ve already done a lot of the work for this during the body part so it shouldn’t be anything out of the ordinary.
Make sure you reiterate what you said in the introduction and sometimes add some more commentary on particular things that could/can serve as relevant examples for future research or topics to investigate further.
One thing which is especially important here is presenting results/data clearly and making them easy to understand even for people who aren’t statisticians themselves. This can help greatly with potential critics.
A statistics research paper, then, should include a summary of the methods used to gather and analyze your data (usually presented in sections 2 & 3), followed by your findings (usually presented in sections 4 – 7). All of this material must be contained within a single document.
Your paper is organized differently from other types of research reports. The most common order has been presented here so as to make it easier for you to adapt these sections back into your own planning:
- Introduction – this section is often called the “literature review” and it contains a brief overview of your topic, including noteworthy definitions and facts. This discussion usually includes information regarding the shortcomings of prior research in this area, if there are any..
- Methodology – this section includes a description of how you obtained (gathered) your data; examples would include surveys, interviews or usage logs..
- Data – if you did not collect data yourself, then consider presenting a chart from an existing source that will help readers understand your results..
- Summary & Discussion – here you’ll present the most important numerical findings related to your study..
- Conclusions & Recommendations – be sure to make recommendations based on your findings.
- References – provide a list of pertinent sources from which you obtained information and ideas..
- Data Analysis – this section is usually presented as a subsection of the data section. Here you’ll report how you analyzed your data, including all calculations and inferences..
- Appendices – this is an optional section in which to place tables that provide detailed information about your study..
- Acknowledgments – it’s always nice to thank someone for their help (in this case, by providing a list of everyone who contributed to the project)..
- Related Reading – refer interested parties to other journal articles, books or websites related to this material for further reading..
- Tables – these are necessary in order to present your results. They also summarize large amounts of information in a small amount of space..
- Figures – Graphs and charts provide data in an easy to understand manner.
- Graphs – A graph is a visual representation of data..
- Charts – A chart is a graphical display of numerical comparisons. It can be useful for showing the relationship between two or more things, such as trends over time.
- List Of Participants – this is a list of everyone who helped you with the project.
- Certificate Of Approval – every department or institute has its own rules about what must be included here: usually some statement saying that your research followed ethical standards and was approved by the proper authorities..
Note: The examples provided above are not intended to represent all possible formats that could be used but rather they provide information about what most researchers tend to do.
Also note: Depending on your course or assignment, you might be required to use certain formatting styles or these requirements may vary from one class or professor to another. But it’s always important to know how and when to cite (reference) sources within your paper.
The last few sections are optional, depending on the format guidelines established by the instructor for your assignment..
Good luck with this paper!
Statistics research paper outline template
The following is the general format and structure of a statistics research paper: introduction/background, problem statement, objectives, materials & methods, results and discussion sections. Use this table of contents as an outline when you are beginning your research.
As you begin each portion make sure to refer back to this outline.
- Problem statement
- Introduction/background
- Materials and methods discussion & results
- Data analysis plan/approach
- Discussion (use subheadings if necessary)
- Conclusion and recommendations
Writing an idealized outline for a statistics research paper. Each of the research paper outline item above has been discussed below in depth.
- Introduction – background information about the topic, relevance of particular issue, significance of data to the problem; 1-2 paragraphs
- Problem statement – state what problem was studied and why – this must be in your initial set of literature review sources (statistics research paper citations); 1 paragraph. State briefly how it relates to overall field or area of study;
- Objectives – list primary and secondary objectives that you were trying to achieve during the course of writing a stats paper; separate each with period. Do not use more than 2 levels of sub-objectives when doing outline.
- Materials & methods – explain your data gathering process (for collection of raw statistics), how you set up the experiment when doing statistical hypothesis testing, and any other relevant information concerning the creation of a statistics research paper; 2-3 paragraphs
- Data analysis/results – state how you analyzed data collected (tables, graphs, charts etc.) that is not available in published works or articles for stats research paper; 2-3 paragraphs
- Discussion section – this is important! Don’t forget to discuss results presented in data analysis/results section as well as recap your problem statement; 3-4 paragraphs. Discussion of main results is an important part of a research paper; don’t forget to include one! Defend your hypothesis (if applicable) and describe its importance. Also compare with other studies done on similar topics; present similarities & differences in data collection methods & outcome measurements used vs others. Take time to explain how each area is different.
- Conclusion & recommendations: In your research paper conclusion , reiterate the significance of your hypothesis; based on results it is either confirmed, disconfirmed or weakened/strengthened. Also indicate what you would like to do now that this is done (if not requested in guidelines) – future research, more studies should be done using same methods etc. Give an indication of how long each study might take and who can benefit from its findings. If needed you also have space here to discuss any recommendations for future work (suggestions are good but don’t sound too pushy).
- References: The references section is usually under a separate heading that includes title & author of the paper/book, date of publication and page numbers. In your bibliography, various books & articles, including editions & versions if needed, that were used as sources when doing your research paper or study (will likely contain other authors’ articles you read).
- Appendices – tables, figures, charts, appendices with raw data, etc. The appendices – anything else that was created during statistical analysis while writing stats research paper can go here (you may need extra sheets of paper if there are too many graphs etc. to include as part of the main text)
Statistics research writing tips:
Make sure to explain your purpose for the study and also give some background information on the problem. This background information should be used as a way of showing why your statistics research paper is important and significant to your field. Here are some ways to say it without coming off as too boring or unprofessional. They’re quite general but good enough:
- “This paper will investigate…”
- “The objective of this study is…”
- “It is not found if…”
- “There have been a few studies concerning..”
- “As there has been much debate about…”
- “No prior study exists that.”
State what your hypothesis was; how you came up with it and any problems you faced trying to prove it.
- Now, state your results and findings, including the statistical analysis if necessary (such as significance or not).
- Write only what was found using charts or graphs, tables, and numbers that back up your claims about the study.
- Check all your spelling and grammar again.
- Pay attention to commas, semicolons and spaces.
- Use a spell checker if possible or ask someone else to proof-read it for you.
This is how simple it is to write a great statistics term paper or research paper. If you get stuck, you can ask for research paper writing help from expert tutors.
Statistics Research Topics
Wondering what to write a research paper on statistics and probability about?
Statistics is a branch of mathematics dealing with the collection, analysis and interpretation or data.
Statistics is used in many fields, including natural science , social sciences, business and engineering.
A statistician collects, computes and analyzes numerical data to summarize information.
If you are looking for a statistics research paper topic ideas you’ve come to the right place!
Check out this list below of major research paper topics in statistics and probability for college students:
Statistics Research Topics – Probability
Description: Probability deals with events that have uncertain outcomes.. It involves mathematical calculations using formulas involving random variables such as probability density functions (pdf), probability distributions , expected values or moments E(x), variance V(X).. In other words a probability distribution summarizes all possible outcomes in terms of probabilities based on theoretical assumptions or data collection.
A specific probability distribution can be constructed from a collection of frequencies of events in the long run.
This is known as the Central Limit Theorem which says that if we take averages of random variables over very large numbers, their distributions will approach normal (i.e. bell-shaped curve) regardless of the shape or other details about them.
It’s also a probability measure used to find out how likely it is that one event A happens given that another event B happened first.
Statistics Research Topics – Descriptive statistics
Description: descriptive statistics collects and interprets numerical data in terms of distributions, graphs, measures and relationships among variables.
For example mean, median and mode are measures of central tendency and dispersion.
On the other hand, standard deviation measures dispersion. This is a statistical technique used to summarize data in terms of its most important features.
Descriptive statistics is also necessary for analyzing real-life situations.
It provides information that’s useful for making business decisions.
Statistics Research Topics – Testing significance of relationships (correlation)
Description: correlation deals with measuring the strength , direction and stability of relationship between two or more variables.
A positive correlation indicates that as the value of one variable increases , so does the value of another variable.
For example, if employees in a call center perform better at work when they are seated close to their supervisors, this would be an example of positive correlation because it that shows that as one variable (seating arrangements) increases the other variable (performance) also increases.
On the other hand, if two variables are negatively correlated which means that as one variable increases, the other decreases.
For example, if some countries have a high GDP per capita and low population growth rate then this would be an example of negative correlation because it shows that as your income rises your population falls.
Statistics – Research Paper – Sampling
Description: sampling deals with determining appropriate sample size s for a study based on specific requirements.
Like when you decide to choose five people out of hundreds in order to conduct surveys or research studies.
The main idea behind sampling is to reduce information loss by minimizing unnecessary information.
Also, sampling is also used to make inferences about a population or to study it indirectly by studying a representative sample (group of people) which is expected to be close enough.
Statistics Research Topics – Hypothesis testing
Description: hypothesis testing deals with conducting statistical tests that determine whether or not the data provided supports certain claim or statistically significant conclusion.
For example if you want to test whether the data shows that girls earn higher grades than boys in math classes.
This would be done using a formal statistical procedure called the t-test. By calculating two values and comparing them we can see if there’s a difference between their means.
If it turns out that one group has an average significantly different from the other, then it’s considered significant.
Statistics – Research Paper – Analysis of variance (ANOVA)
Description: analysis of variance (ANOVA) deals with comparing means between at least three or more groups to determine if they’re similar within a certain margin.
It can also be used for determining whether an overall mean is different than another overall mean across several groups.
ANOVA helps determine if there are significant differences in values that would affect results and conclusions drawn from two or more related populations.
For example, you want to know if there’s any difference among four brands of your favorite soft drink so you conduct an experiment by taking 10 people who all like this particular kind and have them taste each of the brands and see which one they prefer.
Statistics Research Topics – Confidence intervals
Description: confidence intervals are used when conducting statistical studies dealing with hypothesis testing which involves making observations about a population based on data collected.
It is essentially ranges of values meant to indicate variability between certain estimated parameters within a group or group of people.
It helps shows how much uncertainty exists within estimates for a single population parameter.
This is done by adding and subtracting margins of error to the original estimation..
Statistics – Research Topics – Non-parametric tests
Description: non-parametric tests are used when conducting statistical studies to make comparisons between two or more samples using data that’s not completely numerical.
This is usually done by converting scores (e.g., number grades, percentages) into ranks so that you’re able to compare between them easier.
Some examples of these tests includes 1 rank sum test, sign test etc…
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Statistics research paper.
This sample Statistics Research Paper is published for educational and informational purposes only. If you need help writing your assignment, please use our research paper writing service and buy a paper on any topic at affordable price. Also check our tips on how to write a research paper , see the lists of research paper topics , and browse research paper examples .
Statistics is a discipline that deals with data: summarizing them, organizing them, finding patterns, and making inferences. Prior to 1850 the word statistics simply referred to sets of facts, usually numerical, that described aspects of the state; that meaning is still seen in the various sets of government statistics, for example the monthly report on the nation’s unemployment rate and the voluminous tables produced in the wake of each decennial census. During the twentieth century, as a result of the work of Karl Pearson, Ronald Fisher, Jerzy Neyman, Egon Pearson, John Tukey, and others, the term came to be used much more broadly to include theories and techniques for the presentation and analyses of such data and for drawing inferences from them. Two works by Stephen Stigler, The History of Statistics: The Measurement of Uncertainty before 1900 (1986) and Statistics on the Table: The History of Statistical Concepts and Methods (1999) offer broad and readable accounts of the history of statistics.
Although often taught in departments of mathematics, statistics is much more than a branch of applied mathematics. It uses the mathematics of probability theory in many of its applications and finite mathematics and the calculus to derive many of its basic theoretical concepts, but it is a separate discipline that requires its practitioners to understand data as well as mathematics.
Variability
In a sense, statistics is mainly concerned with variability. If every object of the same class were the same, we would have no need for statistics. If all peas were indeed alike, we could measure just one and know all about peas. If all families reacted similarly to an income supplement, we would have no need to mount a large scale negative income tax experiment. If all individuals held the same opinion on an issue of the day, we would only need to ask one person’s opinion and we would need to take no particular care in how we chose that person. Variability, however, is a fact of life and so statistics is needed to help reveal patterns in the face of variability.
Statistics is used in the collection of data in several ways. If the data are to be collected via an experiment, statistical theory directs how to design that experiment in such a way that it will yield maximum information. The principles of replication (to allow the measurement of variability), control (to eliminate known sources of extraneous variability), and randomization (to “even out” unknown sources of variation) as enunciated by Fisher in his 1935 book The Design of Experiments help ensure that if differences are found between those receiving experimental treatment(s) and those in control group(s), those differences can be attributed to the treatment(s) rather than to preexisting differences between the groups or to experimental error. If the data are to be collected via a sample survey, the principles of probability sampling ensure that the findings can be generalized to the population from which the sample was drawn. Variations on simple random sampling (which is analogous to drawing numbers out of a hat) take advantage of known properties of a population in order to make the sampling more efficient. The technique of stratified sampling is analogous to blocking in experimental design and takes advantage of similarities in units of the population to control variability.
Once data are collected, via experiments, sample surveys, censuses, or other means, they rarely speak for themselves. There is variability, owing to the intrinsic variability of the units themselves or to their reactions to the experimental treatments, or to errors made in the measuring process itself. Statistical techniques for measuring the central tendency of a variable (e.g., means, medians) clear away variability and make it possible to view patterns and make comparisons across groups. Measures of the variability of a variable (e.g., ranges and standard deviations) give information on the spread of the data— within a group and in comparisons between groups. There are also summarization techniques of correlation and regression to display the patterns of relations between variables—for example, how does a nation’s GDP per capita relate to its literacy rate? These numerical techniques work hand in hand with graphical techniques (e.g., histograms, scattergrams) to reveal patterns in the data. Indeed, using numerical summaries without examining graphical representations of the data can often be misleading. Of course, there are many more complicated and sophisticated summary measures (e.g., multiple regression) and graphical techniques (e.g., residual plots) that aid in the summarization of data. Much of modern data analysis, especially as developed by John Tukey, relies on less conventional measures, on transformations of data, and on novel graphical techniques. Such procedures as correspondence analysis and data mining harness the power of modern computing to search for patterns in very large datasets.
Frequentist And Bayesian Inference
Perhaps the most important use of statistics, however, is in making inferences. One is rarely interested merely in reactions of subjects in an experiment or the answers from members of a sample; instead one wishes to make generalizations to people who are like the experimental subjects or inferences about the population from which the sample was drawn. There are two major modes of making such inference.
Classical or frequentist inference (the mode that has been most often taught and used in the social sciences) conceptualizes the current experiment or sample as one from an infinite number of such procedures carried out in the same way. It then uses the principles codified by Fisher and refined by Neyman and Pearson to ask whether the differences found in an experiment or from a sample survey are sufficiently large to be unlikely to have happened by mere chance. Specifically it takes the stance of positing a null hypothesis that is the opposite of what the investigator believes to be true and has set out to prove. If the outcome of the experiment (or the sample quantity) or one more extreme is unlikely to have occurred if the null hypothesis is true, then the null hypothesis is rejected. Conventionally if the probability of the outcome (or one more extreme) occurring when the null hypothesis is true is less than .05 (or sometimes .01), then the result is declared “statistically significant.”
Frequentists also carry out estimation by putting a confidence interval around a quantity measured from the sample to infer what the corresponding quantity in the population is. For example, if a sample survey reports the percentage in the sample who favor a particular candidate to be 55 percent and gives a 95 percent confidence interval as 52 to 58 percent, the meaning is that a procedure has been followed that gives an interval that covers the true population percent 95 percent of the time. The frequentist does not know (and is not able to put a probability on) whether in any particular case the interval covers the true population percent—the confidence is in the procedure, not in the interval itself. Further, the interval takes into account only what is known as sampling error, the variation among the conceptually infinite number of replications of the current procedure. It does not take into account non-sampling error arising from such problems in data collection as poorly worded questions, nonre-sponse, and attrition from a sample.
In order for these mechanisms of classical statistics to be used appropriately, a probability mechanism (probability sampling or randomization) must have been used to collect the data. In the social sciences this caution is often ignored; statistical inference is performed on data collected via non-probabilistic means and even on complete enumerations. There is little statistical theory to justify such applications, although superpopulation models are sometimes invoked to justify them and social scientists often argue that the means by which the data were accumulated resemble a random process.
Since the 1970s there has been a major renewal of interest in what was historically called inverse probability and is currently called Bayesian inference (after the English nonconformist minister and—during his lifetime— unpublished mathematician Thomas Bayes [1701?— 1761]). Admitting the experimenter’s or analyst’s subjective prior distribution formally into the analysis, Bayesian inference uses Bayes’ theorem (which is an accepted theorem of probability for both frequentists and Bayesians) to combine the prior distribution with the data from the current investigation to update the probability that the hypotheses being investigated is true. Note that Bayesians do speak of the probability of a hypothesis being true while frequentists must phrase their conclusions in terms of the probability of outcomes when the null hypothesis is true. Further, Bayesians construct credibility intervals, for which, unlike the frequentists’ confidence intervals, it is proper to speak of the probability that the population quantity falls in the interval, because in the Bayesian stance population parameters are viewed as having probability distributions. For a frequentist, a population parameter is a fixed, albeit usually unknown, constant. Much of the revival of interest in Bayesian analysis has happened in the wake of advances in computing that make it possible to use approximations of previously intractable models.
While the distinction between Bayesians and frequentists has been fairly sharp, as Stephen E. Fienberg and Joseph B. Kadane (2001) note the two schools are coming together, with Bayesians paying increasing attention to frequentist properties of Bayesian procedures and frequen-tists increasingly using hierarchical models.
Two much more detailed descriptions of the field of statistics and its ramifications than is possible here are given by William H. Kruskal (1968) and Fienberg and Kadane (2001).
Bibliography:
- Fienberg, Stephen E., and Joseph B. Kadane. 2001. Statistics: The Field. In International Encyclopedia of the Social and Behavioral Sciences, ed. Neil J. Smelser and Paul B. Baltes, 15085–15090. Oxford, U.K.: Elsevier.
- Fisher, Ronald A. 1935. The Design of Experiments. Edinburgh: Oliver and Boyd.
- Kruskal, William H. 1968. Statistics: The Field. In International Encyclopedia of the Social Sciences, ed. David L. Sills, vol. 15, 206–224. New York: Macmillan.
- Stigler, Stephen M. 1986. The History of Statistics: The Measurement of Uncertainty before 1900. Cambridge, MA: Harvard University Press.
- Stigler, Stephen M. 1999. Statistics on the Table: The History of Statistical Concepts and Methods. Cambridge, MA: Harvard University Press.
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A time series is a stretch of values on the same scale indexed by a time-like parameter. The basic data and parameters are functions.
Time series take on a dazzling variety of shapes and forms, indeed there are as many time series as there are functions of real numbers. Some common examples of time series forms are provided in Fig. 1. One notes periods, trends, wandering and integer-values. The time series such as those in the figure may be contemporaneous and a goal may be to understand the interrelationships.
Concepts and fields related to time series include: logitudinal data, growth curves, repeated measures, econometric models, multivariate analysis, signal processing, and systems analysis.
The field, time series analysis, consists of the techniques which when applied to time series lead to improved knowledge. The purposes include summary, decision, description, prediction.
The field has a theoretical side and an applied side. The former is part of the theory of stochastic processes (e.g., representations, prediction, information, limit theorems) while applications often involve extensions of techniques of ‘ordinary’ statistics, for example, regression, analysis of variance, multivariate analysis, sampling. The field is renowned for jargon and acronyms—white noise, cepstrum, ARMA, ARCH (see Granger 1982).
1. Importance
‘… but time and chance happeneth to them all’ (Ecclesiastes).
Time series ideas appear basic to virtually all activities. Time series are used by nature and humans alike for communication, description, and visualization. Because time is a physical concept, parameters and other characteristics is mathematical models for time series can have real-world interpretations. This is of great assistance in the analysis and synthesis of time series.
Time series are basic to scientific investigations. There are: circadian rhythms, seasonal behaviors, trends, changes, and evolving behavior to be studied and understood. Basic questions of scientific concern are formulated in terms of time series concepts— Predicted value? Leading? Lagging? Causal connection? Description? Association? Autocorrelation? Signal? Seasonal effect? New phenomenon? Control? Periodic? Changing? Trending? Hidden period? Cycles?
Because of the tremendous variety of possibilities, substantial simplifications are needed in many time series analyses. These may include assumptions of stationarity, mixing or asymptotic independence, normality, linearity. Luckily such assumptions often appear plausible in practice.
The subject of time series analysis would be important if for no other reason than that it provides means of examining the basic assumption of statistical independence invariably made in ordinary statistics. One of the first commonly used procedures for this problem was the Durbin–Watson test (Durbin and Watson 1950). The autocovariance and spectrum functions, see below, are now often used in this context.
Figure 2 The dashed lines provide 95 percent bounds about a central curve
Contemporary time series analysis has substantial beginnings in both the physical and social sciences. Basic concepts have appeared in each subject and made their way to the other with consequent transfer of technology. Historical researchers important in the development of the field include: Lauritzen (1981), Hooker (1901), Einstein (1914), Wiener (1949), Yule (1927), Fisher (1929), Tukey (1984), Whittle (1951), Bartlett (1966). Books particularly influential in the social sciences include Moore (1914) and Davis (1941). Nowadays many early analyses appear naive. For example, Beveridge in 1920 listed some 19 periods for a wheat price index running from 1500 to 1869 (Beveridge 1922). It is hard to imagine the presence of so much regular behavior in such a series. When statistical uncertainty is estimated using latter-day techniques most of these periods appear insignificant; see the periodogram with 95 percent error bounds in Fig. 2. Many historical references are included in Wold (1965).
Historians of science have made some surprising discoveries concerning early work with time series. An example is presented in Tufte (1983). He shows a time series plot from the tenth or eleventh century AD. This graph is speculated to provide movements of the planets and sun. It is remarkable for its cartesian type character. More generally Tufte remarks, following a study of newspapers and magazines, that ‘The timeseries plot is the most frequently used form of graphic design.’ Casual observation certainly supports Tufte’s study.
Important problems that were addressed in the twentieth century include: seasonal adjustment, leading and lagging indicators, and index numbers. Paradigms that were developed include:
series = signal + noise
series = trend seasonal + noise
series = sum of cosines + noise
series = regression function + noise
These conceptualizations have been used for forecasting, seasonal adjustment and description amongst other things. There are surprises, for example, ordinary least squares is surprisingly efficient in the time series case (see Grenander and Rosenblatt 1957). Other books from the 1950s and 1960s that proved important are Blackman and Tukey (1959), Granger and Hatanaka (1964), Box and Jenkins (1970). Important papers from the era include: Akaike (1965), Hannan (1963) and Parzen (1961).
An important development that began in the 1950s and continues today is the use of recursive computation in the state space model; see Kalman (1963), Harvey (1990), Shumway and Stoffer (2000), and Durbin and Koopman (2001).
3.1 Concepts
There are a number of concepts that recur in time series work. Already defined is the time series, a stretch of values on the same scale indexed by a time parameter. The time parameter may range over the positive and negative integers or all real numbers or subsets of these. Time series data refer to a segment of a time series. A time series is said to have a trend when there is a slowly evolving change. It has a seasonal component when some cyclic movement of period one year is present. (The period of a cyclic phenomenon is the amount of time for it to repeat itself. Its frequency is the reciprocal of the period.)
There is an algebra of mathematical operations that either nature or an analyst may apply to a time series {X(t)} to produce another series {Y(t)}. Foremost is the filter, or linear time invariant operation or system. In the case of discrete equispaced time this may be represented as
t, u = 0, + 1, … An example is the running mean used to smooth a series. The functions X(.), Y(.) may be vector-valued and a(.) may be matrix-valued. In the vector-valued case feedback may be present. The sequence a(u) is called the impulse response function. The operation has the surprising property of taking a series of period P into a series of the same period P. The filter is called realizable when a(u) = 0 for u < 0. Such filters appear in causal systems and when the goal is prediction.
The above ideas concern both deterministic and random series. The latter prove to be important in developing solutions to important problems. Specifically it often proves useful to view the subject of time series as part of the general theory of stochastic processes, that is, indexed families of random variables. One writes {Y(t, ω), t in V}, with ω a random variable and V a set of times. Time series data are then viewed as a segment, {Y(t, ω 0 ), t = 0,…, T – 1} of the realization labeled by ω , the obtained value of ω. Stochastic time series {Y(t), t = 0, + 1, + 2, …} sometimes are conveniently described by finite dimensional distributions such as
This is particularly the case for time series values with joint normal distributions.
Time series may also be usefully described or generated by stochastic models involving the independent identically distributed random variables of ordinary statistics.
Stochastic models may be distinguished as parametric or nonparametric. Basic parameters of the nonparametric approach include: moments, joint probability and density functions, mean functions, auto-covariance and cross-covariance functions, power spectra.
One basic assumption of time series analysis is that of stationarity. Here the choice of time origin does not affect the statistical properties of the process. For example, the mean level of a stationary series is constant. Basic to time series analysis is handling temporal dependence. To this end one can define the cross-covariance function of the series X and Y at lag u as the covariance of the values X(t + u) and Y(t). In the stationary case this function does not depend on t. In an early paper, Hooker (1901) computed an estimate of this quantity. Another useful parameter is the power spectrum, a display of the intensity or variability of the phenomenon vs. period or frequency. It may be defined as the Fourier transform of the auto-covariance function. The power spectrum proves useful in displaying the serial dependence present, in discovering periodic phenomena and in diagnosing possible models for a series.
In the parametric case there are the autoregressive moving average (ARMA) series. These are regression-type models expressing the value Y(t) as a linear function of a finite number of past values Y(t – 1), Y(t – 2), … of the series and the values ε(t), ε(t – 1), … of a sequence of independent identically distributed random variables. ARMAs have proved particularly useful in problems of forecasting.
Contemporary models for time series are often based upon the idea of state. This may be defined as the smallest entity summarizing the past history of the process. There are two parts to the state space model. First, a state equation that describes how the value of the state evolves in time. This equation may contain a purely random element. Second there is an equation indicating how the available measurements at that time t come from the state value at that time. It too may involve a purely random element. The concept of the state of a system has been basic to physics for many years in classical dynamics and quantum mechanics. The idea was seized upon by control engineers, for example, Kalman (1963) in the late 1950s. The econometricians realized its usefulness in the early 1980s (see Harvey 1990).
A number of specific probability models have been
studied in depth, including the Gaussian, the ARMA, the bilinear (Granger 1978), various other nonlinear (Tong 1990), long and short memory, ARMAX, ARCH (Einstein 1914), hidden Markov (MacDonald and Zucchini 1997), random walk, stochastic differential equations (Guttorp 1995, Prakasa Rao 1999), and the periodically stationary.
A list of journals where these processes are often discussed is included at the end of this research paper.
3.2 Problems
There are scientific problems and there are associated statistical problems that arise. Methods have been devised for handling many of these. The scientific problems include: smoothing, prediction, association, index numbers, feedback, and control. Specific statistical problems arise directly. Among these are including explanatories in a model, estimation of parameters such as hidden frequencies, uncertainty computation, goodness of fit, and testing.
Special difficulties arise. These include: missing values, censoring, measurement error, irregular sampling, feedback, outliers, shocks, signal-generated noise, trading days, festivals, changing seasonal pattern, aliasing, data observed in two series at different time points.
Particularly important are the problems of association and prediction. The former asks the questions of whether two series are somehow related and what is the strength of any association. Measures of association include: the cross-correlation and the coherence functions. The prediction problem concerns the forecasting of future values. There are useful mathematical formulations of this problem but because of unpredictable human intervention there are situations where guesswork seems just as good.
Theoretical tools employed to address the problems of time series analysis include: mathematical models, asymptotic methods, functional analysis, and transforms.
4.1 Descriptive
Descriptive methods are particularly useful for exploratory and summary purposes. They involve graphs and other displays, simple statistics such as means, medians and percentiles, and the techniques of exploratory data analysis (Tukey 1977).
The most common method of describing a time series is by presenting a graph, see Fig. 1. Such graphs are basic for communication and assessing a situation. There are different types. Cleveland (1985) mentions the connected, symbol, connected-symbol and verticalline displays in particular. Figure 1 presents connected graphs.
Descriptive values derived from time series data include extremes, turning points, level crossings and the periodogram (see Fig. 2 for an example of the latter). Descriptive methods typically involve generating displays using manipulations of time series data via operations such as differencing, smoothing and narrow band filtering.
A display with a long history (Laplace 1825, Wold 1965) is the Buys–Ballot table. Among other things it is useful for studying the presence and character of a phenomenon of period P such as a circadian rhythm. One creates a matrix with entry Y((i – 1)P + j) in row i, column j = 1, …, P and then, for example, one computes column averages. These values provide an estimate of the effect of period P. The graphs of the individual rows may be stacked beneath each other in
a display. This is useful for discerning slowly evolving behavior.
Descriptive methods may be time-side (as when a running mean is computed), frequency-side (as when a periodogram is computed) or hybrid (as when a sliding window periodogram analysis is employed).
4.2 Parameter Estimation
The way to a solution of many time series problems is via the setting down of a stochastic model. Parameters are constants of unknown values included in the model. They are important because substantial advantages arise when one works within the context of a model. These advantages include: estimated standard errors, efficiency, and communication of results. Often parameter estimates are important because they are fed into later procedures, for example, formulas for forecasting.
General methods of estimating parameters include: method of moments, least squares and maximum likelihood. An important time series case is that of harmonic regression. It was developed in Fisher (1929) and Whittle (1951).
There are parametric and nonparametric approaches to estimation. The parametric has the advantage that if the model is correct, then the estimated coefficients have considerably smaller standard errors.
4.3 Uncertainty Estimation
Estimates without some indication of their uncertainty are not particularly useful in practice. There are a variety of methods used in time series analysis to develop uncertainty measures. If maximum likelihood estimation has been used there are classic (asymptotic) formulas. The delta-method or method of linearization is useful if the quantity of interest can be recognized to be a smooth function of other quantities whose variability can be estimated directly. Methods of splitting the data into segments, such as the jackknife, can have time series variants. A method currently enjoying considerable investigation is the bootstrap (Davison and Hinkley 1997). The assumptions made in validating these methods are typically that the series involved is stationary and mixing.
4.4 Seasonal Adjustment
Seasonal adjustment may be defined as the attempted removal of obscuring unobservable annual components. There are many methods of seasonal adjustment (National Bureau of Economic Research 1976), including state space approaches (Hannan 1963).
The power spectrum provides one means of assessing the effects of various suggested adjustment procedures.
4.5 System Identification
System identification refers to the problem of obtaining a description or model of a system on the basis of a stretch of input to and the corresponding output from a system of interest. The system may be assumed to be linear time invariant as defined above.
In designed experiments the input may be a series of pulses, a sinusoid or white noise. In a natural experiment the input is not under the control of the investigator and this leads to complications in the interpretation of the results. System identification relates to the issue of causality. In some systems one can turn the input off and on and things are clearer.
4.6 Computing
Important computer packages for time series analysis include: Splus, Matlab, Mathematica, SAS, SPSS, TSP, STAMP. Some surprising algorithms have been found: the fast Fourier transform (FFT), fast algorithms from computational geometry, Monte Carlo methods, and the Kalman–Bucy filter. Amazingly a variant of the latter was employed by Thiele in 1880 Lauritzen 1981), while the FFT was known to Gauss in the early 1800s (Heideman et al. 1984).
5. Current Theory And Research
Much of what is being developed in current theory and research is driven by what goes on in practice in time series analyses. What is involved in a time series analysis? The elements include: the question, the experiment, the data, plotting the data, the model, model validation, and model use. The importance of recognizing and assessing the basic assumptions is fundamental.
The approach adopted in practice often depends on how much data are available. In the case that there are a lot of data even procedures that are in some sense inefficient are often used effectively. A change from the past is that contemporary analysis often results from the appearance of very large fine data sets. The amount of data can seem limitless as, for example, in the case of records of computer tasks. There are many hot research topics. One can mention: exploratory data analysis techniques for very large data sets, so-called improved estimates, testing (association? cycle present?), goodness of fit diagnostics. There are the classical and Bayesian approaches, the parametric, semi-parametric, and nonparametric analyses, the problem of dimension estimation and that of reexpression of variables.
Current efforts include research into: bootstrap variants, long-memory processes, long-tailed distributions, nonGaussian processes, unit roots, nonlinearities, better approximations to distributions, demonstrations of efficiency, self-similar processes, scaling relationships, irregularly observed data, doubly stochastic processes as in hidden Markov, cointegration, disaggregation, cyclic stationarity, wavelets, and particularly inference for stochastic differential equations.
Today’s time series data values may be general linear model type (Fahrmein and Tutz 1994), for example, ordinal, state-valued, counts, proportions, angles, ranks. They may be vectors. They may be functions. The time label t may be location in space or even a function. The series may be vector-valued. The data may have been collected in an experimental design.
There are some surprises: the necessity of tapering and prewhitening to reduce bias, the occurrence of aliasing, the high efficiency of ordinary least squares estimates in the correlated case, and the approximate independence of empirical Fourier transform values at different frequencies (Brillinger 1975).
6. Future Directions
It seems clear that time series research will continue on all the existing topics as the assumptions on which any existing problem solution has been based appear inadequate. Further, it can be anticipated that more researchers from nontraditional areas will become interested in the area of time series as they realize that the data they have collected, or will be collecting, are correlated in time.
Researchers can be expected to be even more concerned with the topics of nonlinearity, conditional heteroscedasticity, inverse problems, long memory, long tails, uncertainty estimation, inclusion of explanatories, new analytic models, and properties of the estimates when the model is not true. The motivation for the latter is that time series with unusual structure seem to appear steadily. An example is the data being collected automatically via the World Wide Web. Researchers can be anticipated to be seeking extensions of existing time series methods to processes with more general definitions of the time label— spatial, spatial-temporal, functional, angular. At the same time they will work on processes whose values are more general, even abstract.
More efficient, more robust, and more applicable solutions will be found for existing problems. Techniques will be developed for dealing with special difficulties such as missing data, nonstationarity, outliers. Better approximations to the distributions of time series based statistics will be developed.
Many have stressed the advantages of linear system identification via white noise input. Wiener (1958) stressed the benefits of using Gaussian white noise input. This idea has not been fully developed. Indeed data sets obtained with this input will continually yield to novel analytic methods as they make their appearance.
The principal journals in which newly developed statistical methods for time series are presented and studied include: Journal of Time Series Analysis, Annals of Statistics, Stochastic Processes and their Applications, Journal of the American Statistical Association, Econometrica, IEEE Transactions in Signal Processing.
Bibliography:
- Akaike H 1965 On the statistical estimation of the frequency response function of a system having multiple input. Annals of the Institute of Statistics and Mathematics 17: 185–210
- Bartlett M S 1966 Stochastic Processes. Cambridge University Press, Cambridge, UK
- Beveridge W H 1922 Wheat prices and rainfall in western Europe. Journal of the Royal Statistical Society 85: 412–59
- Blackman R B, Tukey J W 1959 The Measurement of Power Spectra From the Point of View of Communications Engineering. Dover, New York
- Box G E P, Jenkins G M 1970 Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco
- Brillinger D R 1975 Time Series: Data Analysis and Theory. Holt, Rinehart and Winston, New York
- Cleveland W S 1985 The Elements of Graphing Data. Wadsworth, Belmont, CA
- Davis H T 1941 The Analysis of Economic Time Series. Principia, Bloomington, IN
- Davison A C, Hinkley D V 1997 Bootstrap Methods and Their Application. Cambridge University Press, Cambridge, UK
- Durbin J, Koopman S J 2001 Time Series Analysis by State Space Methods. Oxford University Press, Oxford, UK
- Durbin J, Watson G S 1950 Testing for serial correlation in least squares regression. Biometrika 37: 409–28
- Durbin J, Watson G S 1951 Testing for serial correlation in least squares regression. Biometrika 38: 159–77
- Einstein A 1914 Methode pour la determination des valeurs statistiques d’observations concernant des grandeurs soumises a des fluctuations irregulieres. Archives of Sciences Physiques et Naturelles Series 4 37: 254–5
- Engle R F (ed.) 1995 ARCH Selected Readings. Oxford University Press, Oxford, UK
- Fahrmein L, Tutz G 1994 Multivariate Statistical Modelling Based on Generalized Linear Models. Springer, New York
- Fisher R A 1929 Tests of significance in harmonic analysis. Proceedings of the Royal Society of London A 125: 54–9
- Granger C W J 1982 Acronyms in time series analysis. Journal of Time Series Analysis 2: 103–8
- Granger C W J, Andersen A P 1978 An Introduction to Bilinear Time Series Models. Vandenhoeck und Ruprecht, Gottingen
- Granger C W J, Hatanaka M 1964 Spectral Analysis of Economic Time Series. Princeton University Press, Princeton, NJ
- Grenander U, Rosenblatt M 1957 Statistical Analysis of Stationary Time Series. Wiley, New York
- Guttorp P 1995 Stochastic Modelling of Scientific Data. Chapman & Hall, London
- Hannan E J 1960 Time Series Analysis, Methuen, London
- Hannan E J 1963 Regression for time series. In: Rosenblatt M (ed.) Time Series Analysis. Wiley, New York
- Harvey A C 1990 Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press, Cambridge, UK
- Harvey A C, Koopman S J, Penzer J 1998 Messy time series: A unified approach. Advances in Econometrics 13: 103–43
- Heideman M T, Jondon D H, Burrus C S 1984 Gauss and the history of the fast Fourier transform. Archives in History and Exact Science 34: 265–77
- Hooker R H 1901 Correlation of the marriage-rate with trade. Journal of the Royal Statistical Society 64: 696–703
- Kalman R E 1963 Mathematical description of linear dynamical systems. SIAM Journal of Control 1: 152–92
- Laplace P S 1825 Traite de Mechanique Celeste. Bachelier, Paris
- Lauritzen S L 1981 Time series analysis in 1880: A discussion of contributions made by T. N. Thiele. International Statistical Review 49: 319–31
- MacDonald I L, Zucchini W 1997 Hidden Marko and Other Models for Discretealued Time Series. Chapman and Hall, London
- Moore H L 1914 Economic Cycles: Their Law and Cause. Macmillan, New York
- National Bureau of Economic Research Bureau of the Census 1976 Conference on Seasonal Analysis of Economic Time Series. Dept. of Commerce, Bureau of the Census, Washington, DC
- Parzen E 1961 An approach to time series analysis. Annals of Mathematical Statistics 32: 951–89
- Prakasa Rao B L S 1999 Statistical Inference for Diffusion Type Processes. Arnold, London
- Shumway R H, Stoffer D S 2000 Time Series Analysis and its Applications. Springer, New York
- Tong H 1990 Non-linear Time Series: A Dynamical System Approach. Oxford University Press, New York
- Tufte E R 1983 The Elements of Graphing Data. Graphics Press, Cheshire, CT
- Tukey J W 1977 Exploratory Data Analysis. Addision-Wesley, Reading, MA
- Tukey J W 1984 The Collected Works of John W. Tukey I-II Time Series. Wadsworth, Belmont, CA
- Whittle P 1951 Hypothesis Testing in Time Series Analysis. Almqvist and Wiksell, Uppsala, Sweden
- Wiener N 1949 Time Series. MIT Press, Cambridge, MA
- Wiener N 1958 Nonlinear Problems in Random Theory. MIT Press, Cambridge, MA
- Wold H O A 1965 Bibliography: on Time Series and Stochastic Processes. MIT Press, Cambridge, MA
- Yule G U 1927 On the method of investigating periodicities in disturbed series with special reference to Wolfer’s sunspot numbers. Philosophical Transactions A 226: 267–98
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Table of Contents
Naturally, all-encompassing information about the slightest details of the statistical paper writing cannot be stuffed into one guideline. Still, we will provide a glimpse of the basics of the stats research paper.
What is a stats research paper?
One of the main problems of stats academic research papers is that not all students understand what it is. Put it bluntly, it is an essay that provides an analysis of the gathered statistical data to induce the key points of a specified research issue. Thus, the author of the paper creates a construct of the topic by explaining the statistical data.
Writing a statistics research paper is quite challenging because the sources of data for statistical analysis are quite numerous. These are data mining, biostatistics, quality control, surveys, statistical modelling, etc.
Collecting data for the college research paper analysis is another headache. Research papers of this type call for the data taken from the most reliable and relevant sources because no indeterminate information is inadmissible here.
How to create the perfect statistics research paper example?
If you want to create the paper that can serve as a research paper writing example of well-written statistics research paper example, then here is a guideline that will help you to master this task.
Select the topic
Obviously, work can’t be written without a topic. Therefore, it is essential to come up with the theme that promises interesting statistics, and a possibility to gather enough data for the research. Access to the reliable sources of the research data is also a must.
If you are not confident about the availability of several sources concerning the chosen topic, you’d better choose something else.
Remember to jot down all the needed information for the proper referencing when you use a resource
Data collection
The duration of this stage depends on the number of data sources and the chosen methodology of the data collection. Mind that once you have chosen the method, you should stick to it. Naturally, it is essential to explain your choice of the methodology in your statistics research paper.
Outlining the paper
Creating a rough draft of the paper is your chance to save some time and nerves. Once you’ve done it, you get a clear picture of what to write about and what points should be worked through.
The intro section
This is, perhaps, the most important part of the paper. As this is the most scientific paper from all the papers you will have to write in your studies, it calls for the most logical and clear approach. Thus, your intro should consist of:
- Opening remarks about the field of the research.
- Credits to other researchers who worked on this theme.
- The scientific motivation for the new research .
- An explanation of why existing researches are not sufficient.
- The thesis statement , aka the core idea of the text.
The body of the text (research report, as they say in statistics)
Believe it or not, but many professional writers start such papers from the body. Here you have to place the Methodology Section where you establish the methods of data collection and the results of it. Usually, all main graphs or charts are placed here as a way to convey the results. All additional materials are gathered in the appendices.
The next paragraph of the paper will be the Evaluation of the gathered data . And that’s where the knowledge on how to read statistics in a research paper can come in handy. If you have no clue how to do it, you’re in trouble, to be honest. At least, you should know three concepts: odds ratios, confidence intervals, and p values. You can start searching for them on the web or in B.S.Everitt’s Dictionary of Statistics.
And the last section of the body is Discussion . Here, as the name suggests, you have to discuss the analysis and the results of the research.
The conclusion
This section requires only several sentences where you summarise the findings and highlight the importance of the research. You may also include a suggestion on how to continue or deepen the research of the issue.
Tips on how to write a statistics paper example
Here are some life hacks and shortcuts that you may use to boost your paper:
- Many sources where you take the statistical data , do offer it with the interpretation. Do not waste time on calculations and take the interpretation from there.
- Visuals are the must: always include a graph, chart, or a table to visualize your words.
- If you do not know the statistical procedure and how to interpret the results , never use it in the paper.
- Always put the statistics at the end of the sentence.
- If your paper requires the presentation of your calculations and you are not confident with it, ask a pro to help you.
- Different types of statistical data require proper formatting. Cite statistics properly according to the chosen format.
…Final thoughts
We hope that our guideline on how to write a statistics paper example unveiled the mystery of writing such papers.
But, in the case you still dread stats essays, here is a sound solution: entrust your task to the professionals! Order a paper at trustworthy writing service and enjoy saved time and the great result.
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How to Write a Statistics Research Paper: A Complete Guide
A statistics research paper is a piece of original research in written form, primarily aimed to be published in an academic journal or magazine and reviewed by the author’s peers. There are no strict requirements as to the assignment’s size, format, number of sources to be used and other characteristics – it is an extremely versatile kind of work, and you should always consult the guidelines of your particular college and ask your instructor about the specifications required in your case.
Research papers in statistics are the main method of moving the research in this discipline forward. They are presented for peer review and don’t exist by themselves, but are intended for discussion and criticism. Although your research paper is just a college assignment, it should fully meet the requirements of this standard. For you, this means that you should carefully prepare your writing for potential counter-arguments and refutations. The quality of your writing depends to a very significant degree on how well you manage to prepare to potential opposition to your point of view. To be fully ready for this, you have to study not just the data sets dealing with the primary subject of your paper, but with all the significant texts about it written before you as well.
In this statistics research paper writing guide, we will cover all the factors that define whether your research paper succeeds or fails.
Choice of Topic
This is, unmistakably, one of the most important stages of your work – a poorly chosen topic means harder work and worse results. That’s why you cannot approach this step carelessly.
- Reword your potential topics as questions. Then look at each potential topic and ask yourself: can this question be answered with the help of statistical analysis? For example, ‘Is there life on Mars?’ is a fascinating question, no doubt about it, but the answer to it lies beyond the domain of statistics;
- Check if there is enough data on the topic or if you are going to be able to gather enough of it to support your point. That is why you should analyze the existing body of research before you settle upon a particular topic;
- If you are having trouble singling out a topic you would like to write about, try checking out online academic databases and search engines, like Google Scholar and EBSCO . By running searches related to the general direction of your future paper, you will be able to discern the topics that are already well-researched to avoid inadvertently choosing them. In addition, it may give you valuable ideas on potential research around which you can build your paper;
- Don’t forget to discuss the topic you’ve chosen with your instructor. What may seem like an awesome research direction to you may look completely different at somebody who has seen hundreds of papers and knows what topics work and what do not.
In the end, the topic you choose should at the same time leave you enough space for independent data collection and analysis and provide sufficient background information to build your argument upon.
Try a quicker way
Here are some examples of good topics you may find helpful in your work:
- The Study of Diversity in Motion Picture Industry;
- Statistical Analysis of Home Advantage Influence on Performance of Football Teams;
- Evidence of Reverse Discrimination in American Businesses during the Last Decade;
- Effects of Placement Based on Academic Ability vs. Placement Based on Age in Education;
- Positive and Negative Effects of Net Neutrality.
As you can see from this list of topics, statistical analysis can be applied equally effectively to almost any area of human knowledge.
Data Collection
There are three types of sources to be used when writing statistics papers:
- Experiments and information gathering activities you’ve carried out personally;
- Statistical information and data sets received from reliable sources, such as FedStats or Google Public Data Explorer ;
- Published works by other authors (journal articles, books, newspapers, online sources, etc.).
Probably more than any other types of academic assignments, statistics research papers are dependent on the information gathered by students themselves and not found in published sources. That is why methods of your data collection occupy a significant portion of your assignment.
Be very careful when selecting data collection methods. You may be given some recommendations by your instructor – if so, follow them and don’t try to be original, for they know what works for your particular case and what doesn’t. If there are no recommendations, try to obtain them – if you ask intelligent questions, instructors usually don’t refuse to answer them. You should be as careful as possible when collecting your data. Once the information is collected, it is impossible to correct the mistakes that have been made in the process of gathering it, and the trustworthiness of your entire paper can easily be compromised.
Make sure you provide sufficient information about your data collection methods for your readers to be able to reconstruct your results. Experiments that cannot be repeated aren’t given much value in the academic community.
Introduction
Your introduction should be, on the one hand, based on facts and, on the other hand, be fascinating and thought-provoking, so that the reader becomes interested in the contents of your paper upon reading just a few lines. In addition to that, it should explain the purpose of your work and smoothly lead to the main content of the paper.
Begin with the so-called a hook – a sentence aimed at grabbing the reader’s attention right off the bat. It may be some interesting statistical information, unexpected piece of data, not a very well-known fact – anything that can pique the audience’s interest and motivate them to read on.
Then move to the thesis statement – a short (no longer than a couple of sentences, and just one if possible) summary of your primary idea behind the entire paper. It is important to understand that a thesis statement is not identical to the topic. A topic simply limits the scope of your research. Thesis statement actively declares what your hypothesis is, and the rest of the paper is dedicated to finding out whether your idea about the subject matter is right or wrong. For example, “Statistical Analysis of Health Benefits of Weight Training vs. Aerobics” is a topic. “Aerobic exercises possess much higher relative health benefits for non-professional sportsmen than weight training” can be a thesis statement for a paper on this topic.
Body Paragraphs
What the body of your paper should contain:
- Methodology – what methods of collecting and analyzing the information you’ve employed. Here you not just specify the methods themselves, but describe how you went about gathering data and conducting the necessary experiments. For example, if you used a survey, you should not only mention the method per se, but dwell on how you prepared questions for it, how you chose people to participate in it, how their answers varied depending on various factors and so on;
- Supporting materials – the best statistics papers are supplemented with graphs, charts, diagrams etc., visually supporting your point of view and serving as reliable proof of your findings;
- Topic paragraphs – here you present your findings. Make sure to never exceed the limitation of one idea per paragraph – papers on statistics are complicated enough without your making their structure unnecessary convoluted. If you always introduce just one point per paragraph, it shows the reader what he can expect and makes it easier to perceive what you try to prove.
General Style and Writing Tips: Secrets of Our Best Academic Writers
- Be wary of plagiarism , i.e., using the other writer’s work without acknowledging the authorship. Plagiarism is a very serious violation of academic rules, and can even lead to expulsion in some cases. So don’t use it intentionally – you will be caught – and use plagiarism checkers to make sure you haven’t done it accidentally, e.g., by forgetting to put quotation marks around a borrowed sentence or writing something that is suspiciously similar to another paper on the same topic;
- Try to keep your sentences relatively short (20-25 words), but don’t turn it into a goal in and of itself. If you need a sentence that is longer than that, use it – just try to gravitate to suggested length in general;
- Do not use colloquial words, expression and grammar structures, including contractions. Your writing should look as professional and impersonal as possible;
- On the other hand, don’t try to make your writing overly scientific through the use of longer and more complex words and more cumbersome sentence structures than necessary. You don’t have to make your writing simplistic, but use complicated terminology only when it is truly needed and don’t look for excuses to make your writing look smarter than it is – it is a very obvious trick;
- Be ready to go through several drafts before you achieve the results that satisfy you and will satisfy your instructor. A good statistics research paper requires a lot of work and revisions, so if you find it necessary to introduce serious changes into your work or even rewrite some parts of it completely, it is quite normal;
- Your goal is to find whether the hypothesis expressed in your thesis statement is true or false, not to prove it right no matter what. In statistics writing, it doesn’t matter whether the hypothesis turns out to be false – what your instructor is going to pay attention to is how you reached your conclusion, how you gathered information, how you carried out your analysis. Concentrate on these aspects and let everything else take care of itself;
- Avoid the passive voice unless it is absolutely necessary. Statistics papers are rather dry thanks to their methodology, and the use of passive voice makes them even harder to read and perceive the information they contain;
- Never cite books and other sources you haven’t read. It is quite easy to find out if you are familiar with this or that source, and if it turns out that you don’t know anything else about the source you are quoting, it is going to harm your reputation as a scholar.
A conclusion is a part where you sum up everything you’ve found out by this point, repeat your thesis statement and decide if your original hypothesis turned out to be true or not. There is not a lot to say about this part: you simply sum up your evidence, explain why you believe the research you’ve carried out to be important, make suggestions for further research in the same area and more or less repeat everything you’ve written up to this moment in short.
Post-Writing Tips: Revising and Proofreading Your Research Paper
You can simply reread your entire paper, trying to notice all the mistakes and flaws in your writing as you go along, but a much more efficient approach to the task of proofreading is using a checklist. You may prepare your own based on the blunders you most often make, or use a ready-made list, like this one:
- Check basic formatting: cover sheet, page numbers, bibliography, etc. Do all these elements comply with your style guide? Are there any ambiguous cases? Are you sure everything should be formatted the way you did it? Consult your instructor if in doubt;
- Introduction – is the “hook” sufficient to attract the reader’s attention? Does it correspond logically to the rest of the paper? Does it flow smoothly into the thesis statement?
- Conclusion – does it sum up everything important about your paper without missing anything?
- Body paragraphs – do you introduce no more than a single point in each paragraph? Are they logically connected to each other? Are the points you mention clear and unambiguous?
- Is all the data clear and backed up with statistical information?
- Do your arguments follow each other logically?
- Did you quote all the sources you used in your paper? If you missed some quotation marks even by mistake you can be accused of plagiarism, which can lead to very unpleasant consequences;
- Are you sure of your grammar, syntax and spelling? Are you sure about the meanings of all the words you used? If in doubt, look them up in a dictionary;
- Is the flow of your paper natural? Does it read easily? You may need to ask somebody else to read it in order to find out for sure.
Writing a statistics research paper is a long, hard and complicated job, but following the right work process, you can make your task considerably easier – we hope that these statistics research paper writing tips will be of help the next time you encounter such an assignment.
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Writing with Descriptive Statistics
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This handout explains how to write with statistics including quick tips, writing descriptive statistics, writing inferential statistics, and using visuals with statistics.
Usually there is no good way to write a statistic. It rarely sounds good, and often interrupts the structure or flow of your writing. Oftentimes the best way to write descriptive statistics is to be direct. If you are citing several statistics about the same topic, it may be best to include them all in the same paragraph or section.
The mean of exam two is 77.7. The median is 75, and the mode is 79. Exam two had a standard deviation of 11.6.
Overall the company had another excellent year. We shipped 14.3 tons of fertilizer for the year, and averaged 1.7 tons of fertilizer during the summer months. This is an increase over last year, where we shipped only 13.1 tons of fertilizer, and averaged only 1.4 tons during the summer months. (Standard deviations were as followed: this summer .3 tons, last summer .4 tons).
Some fields prefer to put means and standard deviations in parentheses like this:
If you have lots of statistics to report, you should strongly consider presenting them in tables or some other visual form. You would then highlight statistics of interest in your text, but would not report all of the statistics. See the section on statistics and visuals for more details.
If you have a data set that you are using (such as all the scores from an exam) it would be unusual to include all of the scores in a paper or article. One of the reasons to use statistics is to condense large amounts of information into more manageable chunks; presenting your entire data set defeats this purpose.
At the bare minimum, if you are presenting statistics on a data set, it should include the mean and probably the standard deviation. This is the minimum information needed to get an idea of what the distribution of your data set might look like. How much additional information you include is entirely up to you. In general, don't include information if it is irrelevant to your argument or purpose. If you include statistics that many of your readers would not understand, consider adding the statistics in a footnote or appendix that explains it in more detail.
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Free Statistics Research Paper Sample for You
Type of paper: Research Paper
Topic: Sports , Time , Gender , Gym , Education , Information , Physical Exercise , Difference
Words: 1100
Published: 2020/12/25
Introduction
We all know that exercise brings great benefit to the lives of everyone. Playing sports, you strengthen your immune system, strengthen the muscles have a greater impact on cardiac system of the body. Due to physical exercise each person develops, reinforcing its character. Regular exercise is important for the health of men of any age - they protect from diseases and, as evidenced by various studies, slow the aging process. Exercise helps increase strength and endurance. Aerobics, cycling, tennis, basketball, jogging and skiing develop endurance, burning a lot of calories. They significantly strengthen the cardiovascular system, the foundation of good health. Exercises that produce power form the body and build muscle. Through the power of the body can be carried out a wide variety of actions that will get more out of life. In this case study I would like to investigate the average time spent on sports by males and females and check if there is a significant difference in time spent on sport by gender.
Data Gathering
For the purpose to gather the data I found a gym near my home and spent there a day. During all the day I asked each sportsman how many hours he or she spends on training per week (on average). I recorded the answer and the gender of respondent. That day there were 83 visitors in the gym. My sample consists of 83 observations.
I’m given with the data of 83 observations of two variables: Time – the number of hours spent in gym averagely per week. Gender – the gender of respondent (0 – male, 1 – female). The gathered data is in a table below: Summarizing the Data I begin with a descriptive statistics for hours spent on sport by the factor of gender. Descriptive statistics helps me to understand the distribution of the variables:
Descriptive Statistics: Time
Variable Gender N N* Mean SE Mean StDev Minimum Q1 Median Q3 Time 0 46 0 6,761 0,561 3,808 1,000 3,000 6,500 10,000 1 37 0 6,919 0,567 3,451 1,000 4,500 7,000 9,500
Variable Gender Maximum Mode Mode Time 0 13,000 3 5 1 13,000 6; 7; 8 5 According to the descriptive statistics the typical time spent on sports is 5 hours per week for both males and females (Mode = 5). The average time spent on sports is 6.761h for men and 6.919h for women. The men’s data is more dispersed than women’s (standard deviations are 3.808 and 3.451 respectively).
Graphical visualization of the frequency distribution for each gender is given below:
Statistical Inference In this section I will perform a hypothesis testing to check the claim mentioned in introduction part of the paper. Null hypothesis: There is no significant difference in time spent on sports between the genders Alternative hypothesis: There is a significant difference in time spent on sports between the genders. I test this hypothesis using independent samples Student’s t-test.
Set level of significance alpha:
Perform testing:.
Two-Sample T-Test and CI: Time; Gender Two-sample T for Time Gender N Mean StDev SE Mean 0 46 6,76 3,81 0,56 1 37 6,92 3,45 0,57 Difference = mu (0) - mu (1)
Estimate for difference: -0,158
95% CI for difference: (-1,763; 1,447) T-Test of difference = 0 (vs not =): T-Value = -0,20 P-Value = 0,845 DF = 81 Both use Pooled StDev = 3,6534 Since p-value of the test is 0.845>0.05, I failed to reject the null hypothesis. I have no evidence to say that there is a significant difference in time spent on sports between the genders (at 5% level of significance).
The analysis shows that there is no difference in time spent on sports between women and men. Both genders are spend approximately equal time on gym per week. However, there is a number of factors which could bias my results. The first factor is that the answers of sportsmen were subjective. Each individual reported his or her own opinion about how many hours is being spent on gym. But this estimation is not accurate. Some people feel time better, some feel it worse. Somebody like to lie to make an impression of a great sportsman. It would be better if I could check their indications by my own – if I’d be able to spend a week in a gym observing each sportsman, for how many hours he or she visited the gym. The second factor is that the men’s data is not seems to be normally distributed. It may affect the result of t-test, because the normality of distribution is the basic assumption of the test. This issue may be resolved if I increase my sample (remember Central Limit Theorem). The third factor is that I observe the data only during one day. May be the other days of the week (or even of the year) are characterized with another content of sportsmen and the data may report a significantly different result. The fourth factor is that I have observed only 1 gym. This gym may have preferences for one gender, which bias the number of visitors by gender. It may have more exercise machines for males (for example) and less for females or vice versa. It also could affect the visitors and their attitude to the time spent on gym. This issue could be resolved if I perform my study in different gyms.
Boneau, C. Alan. "The Effects of Violations of Assumptions Underlying the T Test."Psychological Bulletin: 49-64.
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How to Find Statistics for a Research Paper
Last Updated: March 29, 2019 References
This article was co-authored by wikiHow staff writer, Jennifer Mueller, JD . Jennifer Mueller is a wikiHow Content Creator. She specializes in reviewing, fact-checking, and evaluating wikiHow's content to ensure thoroughness and accuracy. Jennifer holds a JD from Indiana University Maurer School of Law in 2006. There are 9 references cited in this article, which can be found at the bottom of the page. This article has been viewed 22,038 times. Learn more...
When you're writing a research paper, particularly in social sciences such as political science or sociology, statistics can help you back up your conclusions with solid data. You typically can find relevant statistics using online sources. However, it's important to accurately assess the reliability of the source. You also need to understand whether the statistics you've found strengthen or undermine your arguments or conclusions before you incorporate them into your writing. [1] X Research source [2] X Trustworthy Source University of North Carolina Writing Center UNC's on-campus and online instructional service that provides assistance to students, faculty, and others during the writing process Go to source
Identifying the Data You Need
- For example, if you're writing a research paper for a sociology class on the effect of crime in inner cities, you may want to make the point that high school graduation rates decrease as the rate of violent crime increases.
- To support that point, you would need data about high school graduation rates in specific inner cities, as well as violent crime rates in the same areas.
- From that data, you would want to find statistics that show the trends in those two rates. Then you can compare those statistics to reach a correlation that would (potentially) support your point.
- Background research also can clue you in to words or phrases that are commonly used by academics, researchers, and statisticians examining the same issues you're discussing in your research paper.
- A basic familiarity with your topic can help you identify additional statistics that you might not have thought of before.
- For example, in reading about the effect of violent crime in inner cities, you may find an article discussing how children coming from high-crime neighborhoods have higher rates of PTSD than children who grow up in peaceful suburbs.
- The issue of PTSD is something you potentially could weave into your research paper, although you'd have to do more digging into the source of the statistics themselves.
- Keep in mind when you're reading on background, this isn't necessarily limited to material that you might use as a source for your research paper. You're just trying to familiarize yourself with the subject generally.
- With a descriptive statistic, those who collected the data got information for every person included in a specific, limited group.
- "Only 2 percent of the students in McKinley High School's senior class have red hair" is an example of a descriptive statistic. All the students in the senior class have been accounted for, and the statistic describes only that group.
- However, if the statisticians used the county high school's senior class as a representative sample of the county as a whole, the result would be an inferential statistic.
- The inferential version would be phrased "According to our study, approximately 2 percent of the people in McKinley County have red hair." The statisticians didn't check the hair color of every person who lived in the county.
- Finding the best key words can be an art form. Using what you learned from your background research, try to use words academics or other researchers in the field use when discussing your topic.
- You not only want to search for specific words, but also synonyms for those words. You also might search for both broader categories and narrower examples of related phenomena.
- For example, "violent crime" is a broad category that may include crimes such as assault, rape, and murder. You may not be able to find statistics that specifically track violent crime generally, but you should be able to find statistics on the murder rate in a given area.
- If you're looking for statistics related to a particular geographic area, you'll need to be flexible there as well. For example, if you can't find statistics that relate solely to a particular neighborhood, you may want to expand outward to the city or even the county.
- While you can run a general internet search using your key words to potentially find statistics you can use in your research paper, knowing specific sources can help you find reliable statistics more quickly.
- For example, if you're looking for statistics related to various demographics in the United States, the U.S. government has many statistics available at www.usa.gov/statistics.
- You also can check the U.S. Census Bureau's website to retrieve census statistics and data.
- The NationMaster website collects data from the CIA World Factbook and other sources to create a wealth of statistics comparing different countries on a number of measures.
Evaluating Sources
- Find out who was responsible for collecting the data, and why. If the organization or group behind the data collection and creation of the statistics has an ideological or political mission, their statistics may be suspect.
- Essentially, if someone is creating statistics to support a particular position or prove their arguments, you cannot trust those statistics. There are many ways raw data can be manipulated to show trends or correlations that don't necessarily reflect reality.
- Government sources typically are highly reliable, as are most university studies. However, even with university studies you want to see if the study was funded in whole or in part by a group or organization with an ideological or political motivation or bias.
- To explore the background adequately, use the journalistic standard of the "5 w's" – who, what, when, where, and why.
- This means you'll want to find out who carried out the study (or, in the case of a poll, who asked the questions), what questions were asked, when was the study or poll conducted, and why the study or poll was conducted.
- The answers to these questions will help you understand the purpose of the statistical research that was conducted, and whether it would be helpful in your own research paper.
- You may find the statistics set forth in a report that describes these statistics and what they mean.
- However, just because someone else has explained the meaning of the statistics doesn't mean you should necessarily take their word for it.
- Draw on your understanding of the background of the study or poll, and look at the interpretation the author presents critically.
- Remove the statistics themselves from the text of the report, for example by copying them into a table. Then you can interpret them on your own without being distracted by the author's interpretation.
- If you create a table of your own from a statistical report, make sure you label it accurately so you can cite the source of the statistics later if you decide to include them in your research paper.
- If you're looking at raw data, you may need to actually calculate the statistics yourself. If you don't have any experience with statistics, talk to someone who does.
- Your teacher or professor may be able to help you understand how to calculate the statistics correctly.
- Even if you have access to a statistics program, there's no guarantee that the result you get actually will be accurate unless you know what information to provide the program. Remember the common phrase with computer programs: "Garbage in, garbage out."
- Don't assume you can just divide two numbers to get a percentage, for example. There are other probability elements that must be taken into account.
Writing with Statistics
- For example, the word "average" is one you often see in everyday writing. However, when you're writing about statistics, the word "average" could mean up to three different things.
- The word "average" can be used to mean the median (the middle value in the set of data), the mean (the result when you add all the values in the set and then divide by the quantity of numbers in the set), or the mode (the number or value in the set that occurs most frequently).
- Therefore, if you read "average," you need to know which of these definitions is meant.
- You also want to make sure that any two or more statistics you're comparing are using the same definition of "average." Not doing so could lead to a significant misinterpretation of your statistics and what they mean in the context of your research.
- Charts and graphs also can be useful even when you are referencing the statistics within your text. Using graphical elements can break up the text and enhance reader understanding.
- Tables, charts, and graphs can be especially beneficial if you ultimately will have to give a presentation of your research paper, either to your class or to teachers or professors.
- As difficult as statistics are to follow in print, they can be even more difficult to follow when someone is merely telling them to you.
- To test the readability of the statistics in your paper, read those paragraphs out loud to yourself. If you find yourself stumbling over them or getting confused as you read, it's likely anyone else will stumble too when reading them for the first time.
- This often has as much to do with how you describe the statistics as the specific statistics you use.
- Keep in mind that numbers themselves are neutral – it is your interpretation of those numbers that gives them meaning.
- For example, if you present the statistic that the murder rate in one neighborhood increased by 500 percent, and in the same period high school graduation rates decreased by 300 percent, these numbers are virtually meaningless without context.
- You don't know what a 500 percent increase entails unless you know what the rate was before the period measured by the statistic.
- When you say "500 percent," it sounds like a large amount, but if there was only one murder before the period measured by the statistic, then what you're actually saying is that during that period there were five murders.
- Additionally, your statistics may be more meaningful if you can compare them to similar statistics in other areas.
- Think of it in terms of a scientific experiment. If scientists are studying the effects of a particular drug to treat a disease, they also include a control group that doesn't take the drug. Comparing the test group to the control group helps show the drug's effectiveness.
- For example, you might write "According to the FBI, violent crime in McKinley County increased by 37 percent between the years 2000 and 2012."
- A textual citation provides immediate authority to the statistics you're using, allowing your readers to trust the statistics and move on to the next point.
- On the other hand, if you don't state where the statistics came from, your reader may be too busy mentally questioning the source of your statistics to fully grasp the point you're trying to make.
Expert Q&A
You might also like.
- ↑ https://owl.english.purdue.edu/owl/resource/672/1/
- ↑ http://writingcenter.unc.edu/handouts/statistics/
- ↑ http://libguides.dixie.edu/c.php?g=57887&p=371711
- ↑ http://www.nationmaster.com/country-info/stats
- ↑ https://www.usa.gov/statistics
- ↑ https://owl.english.purdue.edu/owl/resource/672/02/
- ↑ http://libguides.lib.msu.edu/datastats
- ↑ https://owl.english.purdue.edu/owl/resource/672/06/
- ↑ https://owl.english.purdue.edu/owl/resource/672/04/
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Top 99+ Trending Statistics Research Topics for Students
Being a statistics student, finding the best statistics research topics is quite challenging. But not anymore; find the best statistics research topics now!!!
Statistics is one of the tough subjects because it consists of lots of formulas, equations and many more. Therefore the students need to spend their time to understand these concepts. And when it comes to finding the best statistics research project for their topics, statistics students are always looking for someone to help them.
In this blog, we will share with you the most interesting and trending statistics research topics in 2023. It will not just help you to stand out in your class but also help you to explore more about the world.
As you know, it is always suggested that you should work on interesting topics. That is why we have mentioned the most interesting research topics for college students and high school students. Here in this blog post, we will share with you the list of 99+ awesome statistics research topics.
Why Do We Need to Have Good Statistics Research Topics?
Table of Contents
Having a good research topic will not just help you score good grades, but it will also allow you to finish your project quickly. Because whenever we work on something interesting, our productivity automatically boosts. Thus, you need not invest lots of time and effort, and you can achieve the best with minimal effort and time.
What Are Some Interesting Research Topics?
If we talk about the interesting research topics in statistics, it can vary from student to student. But here are the key topics that are quite interesting for almost every student:-
- Literacy rate in a city.
- Abortion and pregnancy rate in the USA.
- Eating disorders in the citizens.
- Parent role in self-esteem and confidence of the student.
- Uses of AI in our daily life to business corporates.
Top 99+ Trending Statistics Research Topics For 2023
Here in this section, we will tell you more than 99 trending statistics research topics:
Sports Statistics Research Topics
- Statistical analysis for legs and head injuries in Football.
- Statistical analysis for shoulder and knee injuries in MotoGP.
- Deep statistical evaluation for the doping test in sports from the past decade.
- Statistical observation on the performance of athletes in the last Olympics.
- Role and effect of sports in the life of the student.
Psychology Research Topics for Statistics
- Deep statistical analysis of the effect of obesity on the student’s mental health in high school and college students.
- Statistical evolution to find out the suicide reason among students and adults.
- Statistics analysis to find out the effect of divorce on children in a country.
- Psychology affects women because of the gender gap in specific country areas.
- Statistics analysis to find out the cause of online bullying in students’ lives.
- In Psychology, PTSD and descriptive tendencies are discussed.
- The function of researchers in statistical testing and probability.
- Acceptable significance and probability thresholds in clinical Psychology.
- The utilization of hypothesis and the role of P 0.05 for improved comprehension.
- What types of statistical data are typically rejected in psychology?
- The application of basic statistical principles and reasoning in psychological analysis.
- The role of correlation is when several psychological concepts are at risk.
- Actual case study learning and modeling are used to generate statistical reports.
- In psychology, naturalistic observation is used as a research sample.
- How should descriptive statistics be used to represent behavioral data sets?
Applied Statistics Research Topics
- Does education have a deep impact on the financial success of an individual?
- The investment in digital technology is having a meaningful return for corporations?
- The gap of financial wealth between rich and poor in the USA.
- A statistical approach to identify the effects of high-frequency trading in financial markets.
- Statistics analysis to determine the impact of the multi-agent model in financial markets.
Personalized Medicine Statistics Research Topics
- Statistical analysis on the effect of methamphetamine on substance abusers.
- Deep research on the impact of the Corona vaccine on the Omnicrone variant.
- Find out the best cancer treatment approach between orthodox therapies and alternative therapies.
- Statistics analysis to identify the role of genes in the child’s overall immunity.
- What factors help the patients to survive from Coronavirus .
Experimental Design Statistics Research Topics
- Generic vs private education is one of the best for the students and has better financial return.
- Psychology vs physiology: which leads the person not to quit their addictions?
- Effect of breastmilk vs packed milk on the infant child overall development
- Which causes more accidents: male alcoholics vs female alcoholics.
- What causes the student not to reveal the cyberbullying in front of their parents in most cases.
Easy Statistics Research Topics
- Application of statistics in the world of data science
- Statistics for finance: how statistics is helping the company to grow their finance
- Advantages and disadvantages of Radar chart
- Minor marriages in south-east Asia and African countries.
- Discussion of ANOVA and correlation.
- What statistical methods are most effective for active sports?
- When measuring the correctness of college tests, a ranking statistical approach is used.
- Statistics play an important role in Data Mining operations.
- The practical application of heat estimation in engineering fields.
- In the field of speech recognition, statistical analysis is used.
- Estimating probiotics: how much time is necessary for an accurate statistical sample?
- How will the United States population grow in the next twenty years?
- The legislation and statistical reports deal with contentious issues.
- The application of empirical entropy approaches with online grammar checking.
- Transparency in statistical methodology and the reporting system of the United States Census Bureau.
Statistical Research Topics for High School
- Uses of statistics in chemometrics
- Statistics in business analytics and business intelligence
- Importance of statistics in physics.
- Deep discussion about multivariate statistics
- Uses of Statistics in machine learning
Survey Topics for Statistics
- Gather the data of the most qualified professionals in a specific area.
- Survey the time wasted by the students in watching Tvs or Netflix.
- Have a survey the fully vaccinated people in the USA
- Gather information on the effect of a government survey on the life of citizens
- Survey to identify the English speakers in the world.
Statistics Research Paper Topics for Graduates
- Have a deep decision of Bayes theorems
- Discuss the Bayesian hierarchical models
- Analysis of the process of Japanese restaurants.
- Deep analysis of Lévy’s continuity theorem
- Analysis of the principle of maximum entropy
AP Statistics Topics
- Discuss about the importance of econometrics
- Analyze the pros and cons of Probit Model
- Types of probability models and their uses
- Deep discussion of ortho stochastic matrix
- Find out the ways to get an adjacency matrix quickly
Good Statistics Research Topics
- National income and the regulation of cryptocurrency.
- The benefits and drawbacks of regression analysis.
- How can estimate methods be used to correct statistical differences?
- Mathematical prediction models vs observation tactics.
- In sociology research, there is bias in quantitative data analysis.
- Inferential analytical approaches vs. descriptive statistics.
- How reliable are AI-based methods in statistical analysis?
- The internet news reporting and the fluctuations: statistics reports.
- The importance of estimate in modeled statistics and artificial sampling.
Business Statistics Topics
- Role of statistics in business in 2023
- Importance of business statistics and analytics
- What is the role of central tendency and dispersion in statistics
- Best process of sampling business data.
- Importance of statistics in big data.
- The characteristics of business data sampling: benefits and cons of software solutions.
- How may two different business tasks be tackled concurrently using linear regression analysis?
- In economic data relations, index numbers, random probability, and correctness are all important.
- The advantages of a dataset approach to statistics in programming statistics.
- Commercial statistics: how should the data be prepared for maximum accuracy?
Statistical Research Topics for College Students
- Evaluate the role of John Tukey’s contribution to statistics.
- The role of statistics to improve ADHD treatment.
- The uses and timeline of probability in statistics.
- Deep analysis of Gertrude Cox’s experimental design in statistics.
- Discuss about Florence Nightingale in statistics.
- What sorts of music do college students prefer?
- The Main Effect of Different Subjects on Student Performance.
- The Importance of Analytics in Statistics Research.
- The Influence of a Better Student in Class.
- Do extracurricular activities help in the transformation of personalities?
- Backbenchers’ Impact on Class Performance.
- Medication’s Importance in Class Performance.
- Are e-books better than traditional books?
- Choosing aspects of a subject in college
How To Write Good Statistics Research Topics?
So, the main question that arises here is how you can write good statistics research topics. The trick is understanding the methodology that is used to collect and interpret statistical data. However, if you are trying to pick any topic for your statistics project, you must think about it before going any further.
As a result, it will teach you about the data types that will be researched because the sample will be chosen correctly. On the other hand, your basic outline for choosing the correct topics is as follows:
- Introduction of a problem
- Methodology explanation and choice.
- Statistical research itself is in the main part (Body Part).
- Samples deviations and variables.
- Lastly, statistical interpretation is your last part (conclusion).
Note: Always include the sources from which you obtained the statistics data.
Top 3 Tips to Choose Good Statistics Research Topics
It can be quite easy for some students to pick a good statistics research topic without the help of an essay writer . But we know that it is not a common scenario for every student. That is why we will mention some of the best tips that will help you choose good statistics research topics for your next project. Either you are in a hurry or have enough time to explore. These tips will help you in every scenario.
1. Narrow down your research topic
We all start with many topics as we are not sure about our specific interests or niche. The initial step to picking up a good research topic for college or school students is to narrow down the research topic.
For this, you need to categorize the matter first. And then pick a specific category as per your interest. After that, brainstorm about the topic’s content and how you can make the points catchy, focused, directional, clear, and specific.
2. Choose a topic that gives you curiosity
After categorizing the statistics research topics, it is time to pick one from the category. Don’t pick the most common topic because it will not help your grades and knowledge. Instead of it, please choose the best one, in which you have little information, or you are more likely to explore it.
In a statistics research paper, you always can explore something beyond your studies. By doing this, you will be more energetic to work on this project. And you will also feel glad to get them lots of information you were willing to have but didn’t get because of any reasons.
It will also make your professor happy to see your work. Ultimately it will affect your grades with a positive attitude.
3. Choose a manageable topic
Now you have decided on the topic, but you need to make sure that your research topic should be manageable. You will have limited time and resources to complete your project if you pick one of the deep statistics research topics with massive information.
Then you will struggle at the last moment and most probably not going to finish your project on time. Therefore, spend enough time exploring the topic and have a good idea about the time duration and resources you will use for the project.
Statistics research topics are massive in numbers. Because statistics operations can be performed on anything from our psychology to our fitness. Therefore there are lots more statistics research topics to explore. But if you are not finding it challenging, then you can take the help of our statistics experts . They will help you to pick the most interesting and trending statistics research topics for your projects.
With this help, you can also save your precious time to invest it in something else. You can also come up with a plethora of topics of your choice and we will help you to pick the best one among them. Apart from that, if you are working on a project and you are not sure whether that is the topic that excites you to work on it or not. Then we can also help you to clear all your doubts on the statistics research topic.
Frequently Asked Questions
Q1. what are some good topics for the statistics project.
Have a look at some good topics for statistics projects:- 1. Research the average height and physics of basketball players. 2. Birth and death rate in a specific city or country. 3. Study on the obesity rate of children and adults in the USA. 4. The growth rate of China in the past few years 5. Major causes of injury in Football
Q2. What are the topics in statistics?
Statistics has lots of topics. It is hard to cover all of them in a short answer. But here are the major ones: conditional probability, variance, random variable, probability distributions, common discrete, and many more.
Q3. What are the top 10 research topics?
Here are the top 10 research topics that you can try in 2023:
1. Plant Science 2. Mental health 3. Nutritional Immunology 4. Mood disorders 5. Aging brains 6. Infectious disease 7. Music therapy 8. Political misinformation 9. Canine Connection 10. Sustainable agriculture
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1.4 - example: descriptive statistics, example 1-5: women's health survey (descriptive statistics) section .
Let us take a look at an example. In 1985, the USDA commissioned a study of women’s nutrition. Nutrient intake was measured for a random sample of 737 women aged 25-50 years. The following variables were measured:
- Calcium(mg)
- Vitamin A(μg)
- Vitamin C(mg)
Download the data file: nutrient.txt
Using Technology
We will use the SAS program called to carry out the calculations that we would like to see.
The lines of this program are saved in a simple text file with a .sas file extension. If you have SAS installed on the machine on which you have download this file, it should launch SAS and open the program within the SAS application. Marking up a print out of the SAS program is also a good strategy for learning how this program is put together.
Download the SAS file: nutrient.sas
The video will walk you through the various parts of the code.
The first part of this SAS output, (download below), is the results of the Means Procedure - proc means. Because the SAS output is usually a relatively long document, printing these pages of output out and marking them with notes is highly recommended if not required!
Download the SAS Output file: nutrient2.lst
The first column of the Means Procedure table above gives the variable name. The second column reports the sample size. This is then followed by the sample means (third column) and the sample standard deviations (fourth column) for each variable. I have copied these values into the table below. I have also rounded these numbers a bit to make them easier to use for this example.
Using Minitab
Click on the graphic or the link below to walk through how to find descriptive statistics for the Women's Nutrition dataset in Minitab.
Video: Descriptive Statistics in Minitab
Descriptive Statistics
A summary of the descriptive statistics is given here for ease of reference.
Notice that the standard deviations are large relative to their respective means, especially for Vitamin A & C. This would indicate a high variability among women in nutrient intake. However, whether the standard deviations are relatively large or not, will depend on the context of the application. Skill in interpreting the statistical analysis depends very much on the researcher's subject matter knowledge.
The variance-covariance matrix is also copied into the matrix below.
\(S = \left(\begin{array}{RRRRR}157829.4 & 940.1 & 6075.8 & 102411.1 & 6701.6 \\ 940.1 & 35.8 & 114.1 & 2383.2 & 137.7 \\ 6075.8 & 114.1 & 934.9 & 7330.1 & 477.2 \\ 102411.1 & 2383.2 & 7330.1 & 2668452.4 & 22063.3 \\ 6701.6 & 137.7 & 477.2 & 22063.3 & 5416.3 \end{array}\right)\)
Interpretation
Because this covariance is positive, we see that calcium intake tends to increase with increasing iron intake. The strength of this positive association can only be judged by comparing s 12 to the product of the sample standard deviations for calcium and iron. This comparison is most readily accomplished by looking at the sample correlation between the two variables.
- The sample variances are given by the diagonal elements of S . For example, the variance of iron intake is \(s_{2}^{2}\). 35. 8 mg 2 .
- The covariances are given by the off-diagonal elements of S . For example, the covariance between calcium and iron intake is \(s_{12}\)= 940. 1.
- Note that, the covariances are all positive, indicating that the daily intake of each nutrient increases with increased intake of the remaining nutrients.
Sample Correlations
The sample correlations are included in the table below.
Here we can see that the correlation between each of the variables and themselves are all equal to one, and the off-diagonal elements give the correlation between each of the pairs of variables.
Generally, we look for the strongest correlations first. The results above suggest that protein, iron, and calcium are all positively associated. Each of these three nutrients intake increases with increasing values of the remaining two.
The coefficient of determination is another measure of association and is simply equal to the square of the correlation. For example, in this case, the coefficient of determination between protein and iron is \((0.623)^2\) or about 0.388.
\[r^2_{23} = 0.62337^2 = 0.38859\]
This says that about 39% of the variation in iron intake is explained by protein intake. Or, conversely, 39% of the protein intake is explained by the variation in the iron intake. Both interpretations are equivalent.
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100 Inspiring Statistics Research Topics
Definition and importance of statistics.
The definition of statistics is always different, depending on your subject and methodology. In simple terms, it is a defined study, analysis, and manipulation of data that must be reviewed. The complex part of statistical analysis is drawing conclusions or coming up with reports. Since it all comes down to data interpretation, one must think about choosing good statistics research topics. Start by addressing various scientific, industrial, or social problems. It will make it easier to narrow things down and find the most efficient solution, be it a manual statistical interpretation or software automation. If things do not work for you, remember that you can pay for research paper and receive additional help with calculation or methodology choice. It will also help you to quote every statistical bit of data correctly if it has been taken from an outside source!
How To Write Statistics Research Topics?
The trick here is to know what methodology will be used to collect and interpret statistical data. Even if you have not chosen your statistics project topic, think about it before going any further. It will help you learn about what kind of data will be researched as the sample will be picked correctly. Your basic outline for choosing the right topic should be this way:
- Introduction of a problem or a scenario.
- Methodology choice and explanation.
- Statistical research itself in the body part.
- Samples variables and deviations.
- Statistical interpretation as your conclusion part.
Always provide sources for statistical data that has been referred to if it is not your first-hand obtained data!
100 Research Topics For Statistics
- good statistics research topics .
It must be noted that statistics are required by numerous disciplines these days, which is why choosing something good for your research can deal with anything. Starting with social media analysis to an estimation of students that have passed the exam successfully, all of it can relate to good stat research topics.
- The pros and cons of regression analysis.
- How accurate is the use of AI-based tools in statistical analysis?
- The online news reports and the fluctuations: statistical reports.
- How can statistical discrepancy be fixed with the help of estimation methods?
- National income and the regulation of cryptocurrencies.
- Covid-19 vaccination in the United States and effectiveness of treatments statistics.
- Mathematical prediction models vs observation strategies.
- Bias in quantitative data analysis in Sociology studies.
- Descriptive statistics vs inferential analysis methods.
- Artificial sampling and the role of estimation in modeled statistics.
- Statistics Topics For Research Project
When you have to choose a specific topic that will reflect statistical methods, start by narrowing things down or determining what kind of analysis will be used. You can think about the role that statistical analysis plays in a certain field. See some examples below:
- Social media and sample size determination methods.
- SAS and coefficient of variation mistakes: manual vs automatic calculation.
- Interpretation of statistical results when using SPSS reporting system.
- The role of standard deviation when using Z-test analysis for social subjects.
- The reasons for using ANOVA testing when dealing with online surveys.
- Commercial hypothesis approach to online trading: why statistical analysis won't be accurate.
- How can diversity be explained with the help of statistical analysis?
- The link between playing video games and the violent outbreaks among college students: statistics.
- College loans: the reasons why the numbers increase and the related controversy.
- Analysis of the Markov Chain for determination of statistical limitations.
- Ph.D. Research Topics In Statistics
As a rule, dealing with Ph.D. is supposed to be more challenging, yet statistical methods still remain the same. It is the subject and the data sample set that go through the changes. For example, you can choose electrical engineering for your statistical method to match the Ph.D. academic level.
- The use of statistical analysis in Quantum Physics: pros and cons when accuracy is essential.
- How can numerical calculations help with theoretical polymers.
- Data Assimilation when dealing with Big Data processes: statistical variables.
- The role of probability when one must apply Stochastic Analysis methods.
- Applicable Analysis and the benefits of statistical surveys for social distancing.
- Improvement of prediction methods in aeronautics.
- Cryptocurrencies and the statistical methods for SWIFT transactions.
- Why acceleration in the banking sector can be more harmful than useful today?
- Cell response and genetics: manual analysis and temporal factor.
- Business intelligence analysis methods: human analysis vs automatic computation.
- Easy Statistics Research Topics
If you want to find an easy statistics research area, think about the real-life application of statistics. It will help you choose easier research topics for statistics. Just make sure to provide explanations of how statistical research helps, talk about why it happens, and how exactly.
- The role of statistics in Data Mining processes.
- The real-life use of entropy estimation in engineering fields.
- Statistical analysis in the field of speech recognition.
- Online grammar checking and the use of empirical entropy methods.
- Ranking statistical approach when estimating the accuracy of college exams.
- Estimation of probiotics: how much time is necessary for an accurate statistical sample?
- How will the population of the United States increase in twenty years?
- What statistical methods are most useful for active sports?
- The legislation and the statistical reports dealing with controversial subjects.
- Transparency of statistical methods and the U.S. Census Bureau reporting system.
- Survey Methods Statistics Research Topics
Not a single statistical method may go without some sort of survey method. It is one of the reasons why we have included various statistics research paper topics that are based on surveys and their practical examples.
- The pros and cons of online surveys for business purposes.
- Data collecting and the use of the predefined groups.
- Analysis of strengths of multiple-question survey method: Geographical dependence and statistical survey methods
- Sampling accuracy and the link to wording in survey questions.
- How can statistical studies become more cost-effective by turning to surveys?
- Drawing survey conclusions and imputation techniques.
- The challenges of super population models in healthcare researching.
- What has the Covid-19 pandemic revealed about inaccurate surveys in 2021?
- Differential calculus versus spatial statistical research methods.
- Inner calculation formulas are most commonly used in online surveys.
- Business Statistics Research Topics
When you need something statistical for your business, think about estimation, prognosis, and analysis. In the majority of cases, you shall deal with economics and finances to provide the pros and cons of certain methodologies. See some statistical research topic examples that relate to business matters:
- Economic data analysis when dealing with probabilities.
- Data distribution when working with descriptive samples: violations, bias, and privacy matters.
- Inferential statistics for small business owners: things one must know.
- The peculiarities of business data sampling: pros and cons of software solutions.
- Linear regression analysis: how can two different business projects be approached at once?
- Index numbers, random probability, and accuracy in economic data relations.
- Programming statistics: the benefits of dataset approach to statistics.
- Commercial statistics: how should the information be prepared for the best accuracy?
- Suggestive evidence vs real evidence among business corporations.
- AI-based statistical report analysis: financial calculation vs human estimation methods.
- Applied Statistics Research Topics
If your college professor asked you to deal with applied statistics for your next assignment, have no worries because applied statistics are related to practice. For example, you can provide a certain case or turn to an actual event where statistical practice can or will be used. Once you choose a case study, narrow things down and see our examples:
- The challenges of statistical analysis and unstructured data.
- The pros and cons of text mining methods and educational statistics.
- Scientific approach to analysis vs basic social media analysis methods.
- Statistical software: what kind of data should not pass through classic solutions?
- The field of healthcare and processing of sensitive statistical information.
- Energy sources and statistical estimation of the green energy benefits.
- Changes in politics and turbulence in economic estimation.
- Globalization and statistical information coming from more than one source.
- Composition of a manual statistical report for unstable political environments.
- The percentage of aggression in active sports: behavioral statistics.
- Sports Statistics Research Topics
Regardless of whether you are making bets on sports or want to find out who might win the game by turning to scientific methods, sports statistics is what you will need. See some good statistics research topics about sports to get inspired:
- Why are Pareto charts preferred more compared to bubble charts for baseball series?
- Data analysis in sporting events: a comparison of baseball and basketball statistics.
- Recording live data sets: how can median accuracy be achieved?
- The role of free agencies for ranking purposes: bias in sports statistics.
- The peculiarities of NFL statistician's work and the reporting privacy.
- Why does the average distance gained per running session matter in soccer?
- Past games analysis information vs future estimates.
- The role of the news organization and the live changes to statistical estimation.
- Inter-sportive data and the commonalities encountered by statisticians in NBA and NFL.
- Knowledge of strengths and weaknesses of teams and their importance for accurate statistics in sports.
- Possible Research Topics For Statistics
Here are the possible subjects where statistics can be researched. If you are not good with formulas and mathematical calculations, these inspiring statistical research topics will help you:
- Cyberbullying and common patterns used for online attacks: collecting stats.
- The role of statistics during political campaigns and elections.
- The success ratio of male vs female employees in Amazon Inc.
- The role of sociological estimation for the establishment of statistical data sets.
- How can data mining practices help establish more accurate statistics?
- The role of urgent calculation for statistical reports in the military.
- Estimation of descriptive statistics during Covid-19 times.
- Western and Southern African statistical reporting agencies: case study comparison.
- Statistical bias in politics: why transparency cannot be achieved.
- The use of statistical estimation in mechanical engineering.
- Psychology Research Topics for Statistics
Psychology, Sociology, Healthcare, and Education are among those topics where statistical analysis is essential. Since psychology encompasses philosophy and the constant variables, depending on the case, these statistical psychology topics will be helpful.
- PTSD and understanding of descriptive trends in Psychology.
- Statistical testing and probability's importance: the role of the researchers.
- Acceptable significance and probability levels in clinical Psychology.
- The use of hypothesis and the role of P 0.05 for better comprehension.
- What statistical data is usually rejected in Psychology?
- The use of elementary statistical principles and reasoning in psychological analysis.
- The role of correlation when there are several psychological concepts at play.
- Statistical reporting based on actual case study learning and modeling.
- Naturalistic observation as a scientific sample in Psychology.
- How should descriptive statistics capture behavioral data sets?
Fitting Your Statistical Research Correctly
One of the most common challenges with statistics is knowing where to fit related data without making it look awkward. If you are in such a situation, our friendly experts are here to provide assistance. As they write research papers for money , they provide you with custom-tailored ideas and will help you avoid plagiarism as you refer to existing case studies. If you want to end up with a great statistical research project, asking for additional guidance is only natural! Take your time to research our list of inspiring statistical topics for research paper and get help when necessary!
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Reading through the article provided a vivid reflection on how racism becomes a serious issue in the today society. There are various types of racism the article brings out manifested in micro aggression form. The varied opinions in my mind provide a clear picture of the information relayed in the article through the following analysis. Discrimination concerning race will major in my analysis. First, let me talk about the black guy abused in the Saudi Arabia that has sparked public […]
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Questions and Answers 1. How are families likely to view your age/gender/race/ethnicity/spirituality etc. and what cultural blind spots or considerations do you need to take into account when you start working with a family (or about a family that you know)?Families tend to view a person?’s ideas based on their age. In most cases young persons’ ideas may be discriminated simply because they are young therefore, family members tend to think that the younger you are, the less informed you […]
Inferential Stats Analysis for Psychology
Concerning the data collected, it means that it is easier to draw a valid conclusion regarding the manner in which their variable relates to each group. In this way, it was easier to determine or provide the means of testing the validity of the outcome as well as inferring their characteristics just from a small sample of the participants into a larger one (Goodwin & Goodwin, 2017). In so doing, it implies that it was easier to tell how the […]
Customer Success/Customer Engagement
Introduction Customer success and customer engagement are important concepts in every company or business oriented organization. There are various concerns about the concepts of customer engagement and customer success and also its importance for various companies. However, studies have also taken keen interest on various issues associated with customer engagement through different strategies. From this description therefore, customer engagement is a critical concern for every management with regards to fulfilling the needs of the customers through organizational strategies. This study […]
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Introduction In quantitative methods a systematic empirical observation through statistical, mathematical and computational techniques are important components. Reliability of the data is important in quantitative methods. Data accuracy is affected by a variety of factors which range from the choice of the collection methods to biasness. Data is important in improving several aspects of business it is therefore imperative for any business to carry out quantitative research. The data provided in the appendices can is helpful in determining the relationships […]
Women’s Nonverbal Behavioral Influence on Men’s Door Etiquette on a College Campus
Abstract Research has found that both sartorial and physical aspects of women influence men’s behavior towards and ratings of women. However, both verbal and nonverbal behavior of women has been seldom manipulated and observed with respect to men’s behavior. This study focused on the extent to which the degree of a woman’s smile may help or hinder her chance of receiving help with the door from a man on her college campus. It was hypothesized that the prosocial behavior of […]
Racial Stereotypes in Athletics
The article, Racial Athletic Stereotype Confirmation in College Football Recruiting, can be found in the Journal of Social Psychology and is written by Grant Thomas, Jessica J. Good, and Alexi R. Gross. This article was published in 2015 and it explores the topic of racial stereotypes in the context of college athletic recruitment. They were basically studying if a racial bias could play a role in college athletic recruitment. The researchers’ first hypothesis was that coaches would rate black players […]
College and African American Male: Basketball Athletes
As a freshman in college, I acknowledge and recognize the fact that college can be a challenging experience. The college experience can become even more challenging when you factor in sororities, clubs, fraternities, sports and other school activities. The article that I have decided to use for my analysis is, “College and the African American Male Athlete by Stephen Brown.” Stephen Brown’s main source comes from the book Closing the Education Achievement Gaps for African American Males by Theodore S. […]
Rape Culture and Statistics
“Rape culture perpetuates the belief that victims have contributed to their own victimization and are responsible for what has happened to them. Behaviors commonly associated with rape culture include victim blaming, slut shaming, sexual objectification, trivializing rape, denial of widespread rape, refusing to acknowledge the harm caused by sexual violence, or some combination of these. It has been used to describe and explain behavior within social groups, including prison rape and in conflict areas where war rape is used as […]
Same-Sex Marriage – Statistics
Marriage was determined to be a fundamental right in Baskin and Obergefell. With many fundamental rights, the right should be considered reversible. Individuals can defer their fundamental rights such as the rights to bear arms, speech, and religion. Therefore, deciding not to marry should also be seen as fundamental. Society has always had strong views on marriage. “Most people think it’s important for couples who intend to stay together to be married, but the number of single Americans who want […]
The Relationship between Early Pregnancy and Wages
Abstract The purpose of this research is to investigate the existence of a possible relationship between early pregnancy and wages. Findings within my research may provide policymakers with critical information required to make decisions that may avert premature pregnancy. Furthermore, I hope the findings of my investigation can help motivate policymakers to focus their efforts on groups that are harmed more due to early pregnancy. The regression analyzes cross-sectional data from 2017 which includes all fifty states. Within the study, […]
Gender Wage Inequaity in the United States: Statistics and Solutions
“There is a deeply ingrained ideology amongst people in our society that men are the movers and shakers in the business world. This refers to the point of view that men are limited to working in major companies and businesses, and women are limited to the domestic domain. This may have been a true reflection of life fifty years ago, but today a new trend is developing in American society. The levels of education amongst women are increasing, which leads […]
Discuss the Importance of Data Management in Research
1. Definiton of Key terms Data management is a general term which refers to a part of research process involving organising, structuring, storage and care of data generated during the research process. It is of prime importance in that it is part of good research practice and it has a bearing on the quality of analysis and research output. The University of Edinburgh (2014) defines data management as a general term covering how you organize, structure, store and care for […]
Computer Edit Software, Research Model Building and Research Decision Making
· SPSS(statistical package for the social sciences)~ Basically in computer edit software, we focused of SPSS. SPSS statistics is actually a software package which is used in various purposes like in interactive purpose, or in batches or in statistical analysis. This software name originally stood for statistical package for the social sciences (i.e. SPSS) and it’s basically an application that can aid in quantitative data handling before developing of this software, our researchers had to run statistical tests on […]
Hazard of Climate Changing
Sustainability is more than just a term, it’s the logic of earth and methods/technique a businesses/people must follow to achieve goals that won’t harm the environment in the meanwhile still good socially and increasing the economy. In my paper, I would like to discuss how could the climate change be harmful to sustainability and how it may have an affect on all aspects of the sustainability. According to Reed Karaim in his article about Climate change, he claims that climate […]
Statistics on Adolescent Suicide
What are your fondest memories playing as a young child? Some of us will remember chasing after a soccer ball or throwing a football across the yard. Others may remember jumping up and down erupting with glee while pretending to be a cheerleader or hitting a baseball across the neighbor’s fence with an aluminum bat. However, a few might not remember playing outside or participating in any sports at all because their parents were engulfed with fear of them getting […]
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The United States formally conducts a census of its entire population. Every ten years to analyze various demographics of our society. However, this process is terribly inefficient and can be a tremendous drain of time and financial resource. And even with attempts to include literally every individual. The census is not 100% accurate due to errors such as under-counting or misrepresenting. Think how difficult it would be to accurately measure all customers of a business organization. To do so would […]
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Statistical Papers
Statistical Papers provides a forum for the presentation and critical assessment of statistical methods. In particular, the journal encourages the discussion of methodological foundations as well as potential applications.
This journal stresses statistical methods that have broad applications; however, it does give special attention to statistical methods that are relevant to the economic and social sciences. In addition to original research papers (regular articles), readers will find survey articles, short communications, reports on statistical software, and book reviews.
- Provides a forum for critical assessments of statistical methods
- Fosters discussion of methodological foundations and potential applications
- Stresses statistical methods that have broad applications
- Draws attention to statistical methods that are relevant to the economic and social sciences
- 90% of authors who answered a survey reported that they would definitely publish or probably publish in the journal again
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- Werner G. Müller,
- Carsten Jentsch,
- Shuangzhe Liu,
- Ulrike Schneider
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Issue 1, February 2023
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Instrumental variable estimation of weighted local average treatment effects.
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Clustering and estimation of finite mixture models under bivariate ranked set sampling with application to a breast cancer study
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A method of correction for heaping error in the variables using validation data
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Simulations and predictions of future values in the time-homogeneous load-sharing model
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Essay Service Examples Art Photography
Photorealism: A Research Paper
- Topics: Photography Realism Research
- Words: 1132
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Photography was invented and observed in the year 1839. Photorealism might also could have been a style manner of artwork that usually enclosed on portray drawings and exceptional photographs platform all through artists research an image, and so makes an attempt to create the photo as in any other medium although the time period can be used loosely to provide an explanation for layout in lots of diverse media it’s conjointly accustomed refer in particular to plenty of art work and painters of the social movement that started out within the overdue 1960s and early 1970s. As a full-scale social movement, photorealism emerged from Pop Art, and as a counter to the maximalist other creative movement, similar to the minimalist art movements of the late 1960s and early 1970s. Photorealists use a photograph or many photos to collect the statistics to form their art works. They argued that the utilization of a camera and picture is a recognition of modernism.
However, admittance to the utilization of images in photorealism become met with severe grievance once the motion commenced to acquire momentum in the past due 1960s, regardless of the actual reality that visual devices were used because the fifteenth century to assist artists with their work. The creation of images inside the nineteenth century had three fundamental effects on art portrait and scenic artists had been tested significance to images and artists turned to photography as careers amongst 19th and 20th-century art moves. It’s far nicely recounted that artists used the photo as deliver fabric and as a useful resource, however, they visited nice lengths to disclaim the very fact fearing that their paintings might be misunderstood as imitations and via the photo’s invention artists had been hospitable a good buy of latest experimentation.
Thus, the end result of the discovery of the photograph became an event in art’s records in the direction of the assignment going through the artist because the earliest acknowledged cave drawings making a try to reproduction the scenes they considered. By the time the photorealists started production their bodies of labor the photo became the main definition of recreating fact and abstraction turned into the primary awareness of the art international. Realism continuing as a present day the front, even experiencing a reemergence within the 1930s, however with the aid of the fifties modernist critics and artistic movement had mineralized realism as a significant art undertaking.
Photorealists, as an alternative, were more inspired by using the paintings of pop artists, and reacted against the creative motion. Pop art and photorealism were reactionary moves, stemming from the increasing and in very extremely good quantity abundance of photographic media, that by means of the middle 20th century had adult into such a big improvement that it certainly turned into threatening to reduce the worth of images in artwork. However, while the pop artists have been primarily commenting at the absurdity of loads of the representational manner, photorealists have been trying to reclaim and exalt the worth of a photo.
In photorealism, change and motion ought to be frozen in time that must then be as it should be painted by means of the creator. Photorealists acquire their intellectual imagery and information with the digital camera and picture. Once the photo is developed the author can consistently transfer the picture from the photographic slide onto canvases.
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Commonly that is regularly performed either through sticking out the slide onto the canvas or by way of victimization historic grid strategies. The resulting snap shots are typically direct copies of the primary image however are normally larger than the first picture or slide. This ends in photorealist vogue being tight and specific, generally with a strain on mental imagery that wishes an excessive level of technical artwork and technology to simulate, like reflections in a reflect like surfaces and therefore the geometric rigor of synthetic surround.
By the 1970s, the accepted acknowledgement approximately the genesis of photorealism had driven ahead for step one to the discovery of the camera within the nineteenth century, with the new device displacing painting by using its realism and velocity. Then, approximately a century and a half later, painters started projecting digital camera producing photographs onto canvas and tracing the imagery, growing the primary works of called photorealism. Photorealists normally display a photographed picture on to many sizes canvas and then used an airbrush to recreate the results of a photo revealed on smooth photographic paper.
The concept of the portray become complicated in most cases with the photograph and that the painting was fair-minded with the method of finishing it up. To conceal the painterliness of panorama/portrait scenes with the appearance of images. To are looking for out a way to capture a crisp floor by means of the usage of an airbrush strategies in their many pictures of the well-known American culture.
Photorealist artists try and make paintings that undergo a resemblance to snap shots as near practical. Photorealist creations are as well frequently called via many names as superrealism and hyperrealism. Each of those names normally could pass under their personal particle genre. They are commonly all in generally taken into consideration to be aspect shoots of photo realism raising upon later with their very own precise surroundings of attention. Numerous specialists do speedier, littler scale watercolor investigations of the photo to work out the subtleties of shading and arrangement, before setting out on the strongly point by point generation of an enormous scale photorealist painting. So, as to precisely move the picture from the photo to the canvas, photorealist craftsmen utilize a mechanical or semi-mechanical methods for move, for example, a projector or the matrix framework.
Despite the fact that recently kept for the most part to the jargon of workmanship fans, the terms ‘photorealism’ and ‘photorealistic’ have abruptly become substantially more unmistakable on the grounds that they’ve been applied in the enormously well-known area of PC gaming. Over numerous decades now, every specialized and graphical development in the gaming scene has been one little advance towards what is in actuality the genuine ultimate objective for most of game planners – an ongoing, 3D world that is genuinely unclear from a true scene. Consequently, the utilization of descriptors photorealistic, and now and again photoreal, to depict games with always improved illustrations.
Ways to deal with games structure that evade this pattern, supporting rather a masterful style in which scenes purposely look like works of art or drawings, are correspondingly depicted as non-photorealistic. With many artists that worked within the photorealism era took a deeper meaning to paintings that not only tricked the viewers as in to thinking the painting is a realistic photograph showing the long process of the patient mind recreating a real still life image on to a canvas.
- Rahaman, Hafizur. “Virtual Heritage: Exploring Photorealism”. Academia.edu, https://www.academia.edu/1923755/Virtual_Heritage_Exploring_Photorealism
- “Photorealism Movement Overview”. The Art Story, https://www.theartstory.org/movement/photorealism/
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Example: Descriptive statistics (experiment) After collecting pretest and posttest data from 30 students across the city, you calculate descriptive statistics. Because you have normal distributed data on an interval scale, you tabulate the mean, standard deviation, variance and range.
Example data sheet To keep track of your salt-tolerance experiment, you make a data sheet where you record information about the variables in the experiment, like salt addition and plant health. To gather information about plant responses over time, you can fill out the same data sheet every few days until the end of the experiment.
An example: if we are interested in knowing whether there is a relationship between hospital beds per 1,000 residents and the reading scores of senior citizens, defined as ages 65 and over, in a given city or country, then we must gather data on both variables.
Research Papers / Publications - Department of Statistics and Data Science Research » Research Papers / Publications Research Papers / Publications Chao Gao, Yandi Shen, Ye Zhang (2023), Uncertainty Quantification in The Bradley-Terry-Luce Model, Information and Inference: A Journal of the IMA.
See our collection of statistics research paper examples . These example papers are to help you understanding how to write this type of written assignments. Statistics is a branch of mathematics dealing with the collection, organization, analysis, interpretation and presentation of data.
Statistical Sufficiency Research Paper Censuses Of Population Research Paper Stochastic Models Research Paper Stock Market Predictability Research Paper Structural Equation Modeling Research Paper Survival Analysis Research Paper Systems Modeling Research Paper Nonprobability Sampling Research Paper 1. Introduction
For example, if a sample survey reports the percentage in the sample who favor a particular candidate to be 55 percent and gives a 95 percent confidence interval as 52 to 58 percent, the meaning is that a procedure has been followed that gives an interval that covers the true population percent 95 percent of the time.
Examples List on Statistics Research Paper Two Experimental Designs Applied in the Management Coronary Artery Disease 5 pages (1250 words) , Download 1 , Research Paper Preview sample Pizza Chain Dominos 5 pages (1250 words) , Research Paper Nobody downloaded yet Preview sample sponsored ads Hire a pro to write a paper under your requirements!
The basic data and parameters are functions. Time series take on a dazzling variety of shapes and forms, indeed there are as many time series as there are functions of real numbers. Some common examples of time series forms are provided in Fig. 1. One notes periods, trends, wandering and integer-values. The time series such as those in the ...
Research papers on statistics involve great focus on analysis, proportional inference and interpretation. This section of Researchomatic is therefore focused on bringing to its customers some of the most carefully selected and compiled research papers on statistics that will help students to better understand the subject. Projects Rating
What is a stats research paper? How to create the perfect statistics research paper example? Select the topic Data collection Outlining the paper The intro section The body of the text (research report, as they say in statistics) The conclusion Tips on how to write a statistics paper example
Here are some examples of good topics you may find helpful in your work: The Study of Diversity in Motion Picture Industry; Statistical Analysis of Home Advantage Influence on Performance of Football Teams; Evidence of Reverse Discrimination in American Businesses during the Last Decade;
The mean of exam two is 77.7. The median is 75, and the mode is 79. Exam two had a standard deviation of 11.6. Overall the company had another excellent year. We shipped 14.3 tons of fertilizer for the year, and averaged 1.7 tons of fertilizer during the summer months.
According to the descriptive statistics the typical time spent on sports is 5 hours per week for both males and females (Mode = 5). The average time spent on sports is 6.761h for men and 6.919h for women. The men's data is more dispersed than women's (standard deviations are 3.808 and 3.451 respectively).
For example, if you present the statistic that the murder rate in one neighborhood increased by 500 percent, and in the same period high school graduation rates decreased by 300 percent, these numbers are virtually meaningless without context.
How To Write Good Statistics Research Topics? Top 3 Tips to Choose Good Statistics Research Topics 1. Narrow down your research topic 2. Choose a topic that gives you curiosity 3. Choose a manageable topic Conclusion Frequently Asked Questions Q1. What are some good topics for the Statistics project? Q2. What are the topics in statistics? Q3.
Example 1-5: Women's Health Survey (Descriptive Statistics) Let us take a look at an example. In 1985, the USDA commissioned a study of women's nutrition. Nutrient intake was measured for a random sample of 737 women aged 25-50 years. The following variables were measured: Calcium (mg) Iron (mg) Protein (g) Vitamin A (μg) Vitamin C (mg)
Statistical Research Papers by Topic Annual Reports Research Reports & Studies Statistical Research Papers by Topic Beginning August 1, 2001: Research Report Series - Computing (RRC) View papers from the Research Report Series on Computing (RRC). Research Report Series - Statistics (RRS)
See some statistical research topic examples that relate to business matters: Economic data analysis when dealing with probabilities. Data distribution when working with descriptive samples: violations, bias, and privacy matters. Inferential statistics for small business owners: things one must know.
Descriptive statistics is used in the form of histograms, bar graphs, line graphs, pie charts and other forms of descriptive statistics. Descriptive statistics help in showing how the test scores are distributed. Presenting test score information in descriptive statistics allows for easy comparison, analysis of trends and result evaluation.
Statistics Essays 18 essay samples found Essay examples Essay topics The Effect of Coffee Consumption on the Risk of Hypertension Words: 1629 Pages: 5 4789 ABSTRACT BACKGROUND: hypertension can be defined as a disorder that makes the blood to exert some forces against the walls of the blood vessels.
Statistical Papers provides a forum for the presentation and critical assessment of statistical methods. In particular, the journal encourages the discussion of methodological foundations as well as potential applications. This journal stresses statistical methods that have broad applications; however, it does give special attention to statistical methods that are relevant to the economic and ...
48 samples of this type. No matter how high you rate your writing abilities, it's always an appropriate idea to check out a competently written Research Paper example, especially when you're dealing with a sophisticated Beach topic. This is precisely the case when WowEssays.com database of sample Research Papers on Beach will prove handy.
As a full-scale social movement, photorealism emerged from Pop Art, and as a counter to the maximalist other creative movement, similar to the minimalist art movements of the late 1960s and early 1970s. Photorealists use a photograph or many photos to collect the statistics to form their art works. They argued that the utilization of a camera ...