In our previous tutorials, we discussed simple regression and multiple regression with continuous variables, but what happens when our independent variable is nominal rather than interval?
The data used in this tutorial are again from the More Tweets, More Votes: Social Media as a Quantitative Indicator of Political Behavior study from DiGrazia J, McKelvey K, Bollen J, Rojas F (2013), which investigated the relationship between social media mentions of candidates in the 2010 and 2012 US House elections with actual vote results.

Multiple Regression A prior tutorial described simple regression as a mapping of a single predictor to an outcome variable. This tutorial covers the case when there is more than one independent variable, also known as multiple regression. Although simple regression is a useful tool for extracting information about bivariate relationships that goes beyond what we get from a correlation or t-test, the real power of regression comes from its ability to incorporate multiple independent variables.

Regression is a basic method for predicting values of some dependent variable \((Y)\) as a function of one or more independent variables \((X_i)\). Simple regression describes the case when there is only one predictor, whereas multiple regression has multiple predictors. This tutorial will focus solely on simple regression.
The data used in this tutorial are from the article More Tweets, More Votes: Social Media as a Quantitative Indicator of Political Behavior from DiGrazia, McKelvey, Bollen, and Rojas (2013).

This post outlines the steps for performing a logistic regression in R. The data come from the 2016 American National Election Survey. Code for preparing the data can be found on our github page, and the cleaned data can be downloaded here.
The steps that will be covered are the following:
Check variable codings and distributions Graphically review bivariate associations Fit the logit model Interpret results in terms of odds ratios Interpret results in terms of predicted probabilities The variables we use will be:

This post outlines the steps for performing a logistic regression in SAS. The data come from the 2016 American National Election Survey. Code for preparing the data can be found on our github page, and the cleaned data can be downloaded here.
The steps that will be covered are the following:
Check variable codings and distributions Graphically review bivariate associations Fit the logit model Interpret results in terms of odds ratios Interpret results in terms of predicted probabilities The variables we use will be:

This post outlines the steps for performing a logistic regression in SPSS. The data come from the 2016 American National Election Survey. Code for preparing the data can be found on our github page, and the cleaned data can be downloaded here.
The steps that will be covered are the following:
Check variable codings and distributions Graphically review bivariate associations Fit the logit model in SPSS Interpret results in terms of odds ratios Interpret results in terms of predicted probabilities The variables used will be:

This post outlines the steps for performing a logistic regression in Stata. The data come from the 2016 American National Election Survey. Code for preparing the data can be found on our github page, and the cleaned data can be downloaded here.
The steps that will be covered are the following:
Check variable codings and distributions Graphically review bivariate associations Fit the logit model in Stata Interpret results in terms of odds ratios Interpret results in terms of predicted probabilities The variables we use will be:

For a fuller treatment on what one-way ANOVA is, see our One-Way ANOVA tutorial here. This tutorial will go over how to conduct one-way ANOVA using SAS.
The data we are using can be downloaded here.
We want to study the effectiveness of different treatments on anxiety. We collect a sample of 75 subjects in the following categories:
No treatment (\(n_1\) = 27). Biofeedback (\(n_2\) = 24). Cognitive-behavioral Treatment (\(n_3\) = 24).

This tutorial is going to take the theory learned in our Two-Way ANOVA tutorial and walk through how to apply it using SAS. We will be using the Moore dataset, which can be downloaded here.
This data frame consists of subjects in a “social-psychological experiment who were faced with manipulated disagreement from a partner of either of low or high status. The subjects could either conform to the partner’s judgment or stick with their own judgment.

All three types of \(t\)-tests can be performed in SAS. This tutorial will demonstrate the steps and syntax needed to conduct one sample, two independent samples, and paired samples t-tests.
There are two datafiles used in this tutorial. The iq_wide can be downloaded here, and the iq_long data can be downloaded here. The one-sample and independent samples examples will use the iq_long data, and the paired samples example will use iq_wide.