But, unlike the multiple regression model, the logistic regression model is designed to test binary response variables. Logistic regression is simply another form of the linear regression model, so the basic idea is the same as a multiple regression analysis.
Finally, we introduce you to logistic regression analysis for a binary response variable with multiple explanatory variables.
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We will also teach also you how to test a categorical explanatory variable with more than two categories in a multiple regression analysis. In this session, we will discuss some things that you should keep in mind as you continue to use data analysis in the future. If your research question does not include one quantitative response variable, you can use the same quantitative response variable that you used in Module 2, or you may choose another one from your data set. Doing so will really allow you to experience the power of multiple regression analysis, and will increase your confidence in your ability to test and interpret more complex regression models. Although you need only two explanatory variables to test a multiple regression model, we encourage you to identify more than one additional explanatory variable. When you go back to your codebooks, ask yourself a few questions like “What other variables might explain the association between my explanatory and response variable?” “What other variables might explain more of the variability in my response variable?”, or even “What other explanatory variables might be interesting to explore?” Additional explanatory variables can be either quantitative, categorical, or both. Note that if you have not yet identified additional explanatory variables, you should choose at least one additional explanatory variable from your data set. Finally, you will develop experience using regression diagnostic techniques to evaluate how well your multiple regression model predicts your observed response variable. You will also learn how to account for nonlinear associations in a linear regression model. In this session, you will apply and interpret a multiple regression analysis for a quantitative response variable, and will learn how to use confidence intervals to take into account error in estimating a population parameter. Multiple regression analysis is tool that allows you to expand on your research question, and conduct a more rigorous test of the association between your explanatory and response variable by adding additional quantitative and/or categorical explanatory variables to your linear regression model.
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