Statistics

R has powerful and aesthetic graphics capabilities. In this blog entry, you’ll learn how to use jittered points in R graphics created with ggplot.

The purpose of a Chi squared test is to determine whether two categorical variables are independent of each other. In this blog, we’ll show you how to use Stata to conduct...

The codebook command in Stata is an easy way of learning a lot about the characteristics of a variable. In this blog, we’ll show you how to use and understand the codebook command in...

Let’s say you have a hypothesis about an experiment that has only two possible outcomes, which you can code as 0 and 1. In Stata, you can easily identify the mean and...

You’re probably familiar with a typical Cartesian (x, y) approach to graphing. However, a polar axis is typically more appropriate when working with periodic data. In this blog, we’ll show you how to use a polar axis in R.

The purpose of a chi squared test is to determine whether two categorical variables are independent of each other. In this blog, we’ll show you how to use R to conduct a chi squared test.

For a normally distributed variable, a z score assigns a number to each data point based on its distance, in standard deviations, from the mean. For example, if the mean of normally distributed variable iq is...

Sometimes, you will need to create a single variable from two or more existing variables in R. In this blog, we’ll show you some options for doing so.

A paired t test in Python compares vectors or values that represent matched subjects who are measured on a continuous variable. In this blog entry, we’ll show you how to run a paired t test in R and...

Often, datasets you work with in R can have missing values. In this blog, we’ll show you how to check an R dataset for missing values. Well also show you to use DataEditR if you need to fill in missing data.

In comparison to programs such as Stata and SPSS, R is not necessarily friendly to data editing using a graphic user interface (GUI). Oftentimes, when working in R, you’ll find yourself using code to data edit or...

For a normally distributed variable, a z score assigns a number to each data point based on its distance, in standard deviations, from the mean. For example, if the mean of variable iq is...

Often, there are only two considerations for summary statistics in basic statistical analysis: Providing means and standard deviations; and creating good-looking tables. In this blog, we’ll show you how to use the Stargazer package in Stata to...

There is sometimes a need for continuously measured variables to be converted into factor variables for statistical analysis—for example, for independent t tests or ANOVAs. In this blog, we’ll show you how to...

A paired t test in R compares vectors or values that represent matched subjects who are measured on a continuous variable. In this blog entry, we’ll show you how to run a paired t test in R and combine with it appropriate graphics.

Analysis of variance (ANOVA) exists in order to determine whether there is an effect of an independent variable with more than two levels on a dependent variable that is continuously distributed. ANOVA can be...

One of the advantages of the R statistical language is that you can save not only variables but also complete model results. There are several reasons you can choose to do so, conducting new statistical procedures on saved models.

A one-sample Wilcoxon signed rank test is applied when you are measuring values of a single variable that you are comparing to some test mean and when this variable is not normally distributed. In this blog entry, we’ll show you...

A dummy variable designates subgroups within your analysis, typically based on 0 and 1 values. In this blog, we’ll how you how to create dummy variables from both continuous variables and binary strings in R.

Often, when creating a bar graph, you want to be able to include more than one variable in the display. In this blog entry, we’ll show you how to use Stata to generate bar graphs that track several variables.