  Interactive Course

# Intro to Statistics with R: Multiple Regression

• 3 hours
• 7 Videos
• 31 Exercises
• 27,501 Participants
• 2,400 XP

### Loved by learners at thousands of top companies:      ### Course Description

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.

1. 1

#### A gentle introduction to the principles of multiple regression

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.

2. 3

#### Dummy coding

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.

3. 2

#### Intuition behind estimation of multiple regression coefficients

This chapter is especially for those that haven’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.

1. 1

#### A gentle introduction to the principles of multiple regression

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.

2. 2

#### Intuition behind estimation of multiple regression coefficients

This chapter is especially for those that haven’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.

3. 3

#### Dummy coding

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.

### What do other learners have to say? “I've used other sites, but DataCamp's been the one that I've stuck with.”

Devon Edwards Joseph

Lloyd's Banking Group “DataCamp is the top resource I recommend for learning data science.”

Louis Maiden “DataCamp is by far my favorite website to learn from.”

Ronald Bowers

Decision Science Analytics @ USAA ##### Andrew Conway

Instructor

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.