- 7 Videos
- 31 Exercises
- 3 hours
- 25,897 Participants
- 2400 XP

**Instructor(s):**

Andrew Conway is a Psychology Professor in the Division of Behavioral and Organizational Sciences at Claremont Graduate University in Claremont, California. He has been teaching introduction to statistics for undergraduate students and advanced statistics for graduate students for 20 years, at a variety of institutions, including the University of South Carolina, the University of Illinois in Chicago, and Princeton University.

Multiple regression is a powerful statistical technique, and here you will discover why and how to use it. Part of the course will focus on matrix algebra since it is essential if you want to start estimating regression coefficients in the regression equation. The final chapter will introduce dummy coding as a technique to handle categorical variables.

The first chapter of the module will start with introducing the multiple regression equation, and the multiple correlation coefficient. You will visualize relationships between variables, and learn how to interpret the outcomes of the model.

- A gentle introduction to the principles of multiple regression 50 xp
- Multiple regression: starting off 50 xp
- Multiple regression: visualization of the relationships 100 xp
- Multiple regression: model selection 100 xp
- Multiple regression: beware of redundancy 100 xp
- Multiple regression: interpretation 50 xp
- Multiple regression: interpretation regression constants 50 xp
- Multiple regression: interpretation regression coefficients 50 xp
- Multiple regression: strongest predictor variable 50 xp

This chapter is especially for those that havenâ€™t done matrix algebra before, or for those that need to do a quick refresh on it. If you want to have a basic understanding on how the regression coefficients are estimated all at once in a multiple regression, you need matrix algebra. Step-by-step this chapter will show you how you go in R from a raw matrix data frame to the correlation matrix and the corresponding regression coefficients.

Dummy coding is used to code categorical variables in a regression analysis. Furthermore, dummy coding will also play an important role once you start doing more complex multiple regression analysis like in moderation (module 7). Conceptually, this chapter is not that hard, but dummy coding can become tedious and you have to be careful not to get tricked when doing your analysis. This chapter will show you how to avoid the most common traps.