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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.

A paired t test in Stata compares two columns 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 Stata and...

ETA squared is a measure of effect size that Stata can easily estimate after regression or ANOVA procedures. In this blog, we’ll show you how to use Stata to calculate ETA squared for...

Spearman correlation is an alternative to Pearson correlation that can be used when the relationship between variables is monotonic—for example, in the context of two variables, when the variables...

The distributions of continuous variables can be depicted in numerous ways, including through the use of histograms and box plots. The advantage of a violin plot is...

One of the standard post-regression diagnostic tests is a test for multicollinearity. In this blog, we’ll show you to test for multicollinearity after a regression using Stata’s vif command.

A dummy variable designates subgroups within your analysis. In Stata, one simple way to create dummy variables is to use the i.prefix, as we have shown you. However, you will not be able to use...

Knowing how to generate random numbers can be useful. For example, if you have ordered data that you need to shuffle, random numbers can help you. In this blog entry, we’ll show you to generate random numbers in Stata.

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...

Making sure that your data are appropriately sorted can be a precondition of data analysis. In this blog entry, we’ll show you how to sort your data in Stata.

Making sure that your data are appropriately ordered within a dataset can simplify analysis. In this blog entry, we’ll show you how to order your variables in Stata.

Filtering out unnecessary data values can simplify data analysis. In this blog entry, we’ll show you how to use Stata’s keep command to retain only the variables of interest to you in a given dataset.

Dropping unwanted variables can simplify data analysis. In this blog entry, we’ll show you how to use Stata’s drop command to eliminate unwanted variables from a given dataset.

Dropping unwanted rows can simplify data analysis. In this blog entry, we’ll show you how to use Stata’s drop command to eliminate unwanted rows from a given dataset.

A one-sample t test is applied when you are measuring values of a single variable that you are comparing to some test mean. In this blog entry, we’ll show you how to conduct a one-sample t-test in Stata. 

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 how to...

Cloning variables can be very useful for data analysis. In this blog entry, we’ll show you to clone variables in Stata, both generally and conditionally.