Intermediate Regression in R

Learn to perform linear and logistic regression with multiple explanatory variables.
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4 Hours14 Videos50 Exercises6,179 Learners
4150 XP

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Course Description

Linear regression and logistic regression are the two most widely used statistical models and act like master keys, unlocking the secrets hidden in datasets. This course builds on the skills you gained in "Introduction to Regression in R", covering linear and logistic regression with multiple explanatory variables. Through hands-on exercises, you’ll explore the relationships between variables in real-world datasets, Taiwan house prices and customer churn modeling, and more. By the end of this course, you’ll know how to include multiple explanatory variables in a model, understand how interactions between variables affect predictions, and understand how linear and logistic regression work.

  1. 1

    Parallel Slopes

    Extend your linear regression skills to "parallel slopes" regression, with one numeric and one categorical explanatory variable. This is the first step towards conquering multiple linear regression.
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  2. 2


    Explore the effect of interactions between explanatory variables. Considering interactions allows for more realistic models that can have better predictive power. You'll also deal with Simpson's Paradox: a non-intuitive result that arises when you have multiple explanatory variables.
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  3. 3

    Multiple Linear Regression

    See how modeling, and linear regression in particular, makes it easy to work with more than two explanatory variables. Once you've mastered fitting linear regression models, you'll get to implement your own linear regression algorithm.
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  4. 4

    Multiple Logistic Regression

    Extend your logistic regression skills to multiple explanatory variables. Understand the logistic distribution, which underpins this form of regression. Finally, implement your own logistic regression algorithm.
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In the following tracks
Data ScientistMachine Learning ScientistStatisticianStatistics FundamentalsSupervised Machine Learning
Maggie Matsui
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Richie Cotton

Curriculum Architect at DataCamp
Richie is a Learning Solutions Architect at DataCamp. He has been using R since 2004, in the fields of proteomics, debt collection, and chemical health and safety. He has released almost 30 R packages on CRAN and Bioconductor – most famously the assertive suite of packages – as well as creating and contributing to many others. He also has written two books on R programming, Learning R and Testing R Code.
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