Intermediate Regression with statsmodels in Python

Learn to perform linear and logistic regression with multiple explanatory variables.

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4 Hours14 Videos52 Exercises
4300 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. In this course, you’ll build on the skills you gained in "Introduction to Regression in Python with statsmodels", as you learn about 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, discover how interactions between variables affect predictions, and understand how linear and logistic regression work.

  1. 1

    Parallel Slopes

    Free

    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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    Parallel slopes linear regression
    50 xp
    Fitting a parallel slopes linear regression
    100 xp
    Interpreting parallel slopes coefficients
    100 xp
    Visualizing each explanatory variable
    100 xp
    Visualizing parallel slopes
    100 xp
    Predicting parallel slopes
    50 xp
    Predicting with a parallel slopes model
    100 xp
    Visualizing parallel slopes model predictions
    100 xp
    Manually calculating predictions
    100 xp
    Assessing model performance
    50 xp
    Comparing coefficients of determination
    100 xp
    Comparing residual standard error
    100 xp

Datasets

Ad conversionCustomer churnTaiwan real estateFish measurement dataeBay auctions

Collaborators

Richie CottonMaggie MatsuiAmy Peterson
Maarten Van den Broeck Headshot

Maarten Van den Broeck

Content Developer at DataCamp

Maarten is an aquatic ecologist and teacher by training and a data scientist by profession. After his career as a Ph.D. researcher at KU Leuven, he wished that he had discovered DataCamp sooner. He loves to combine education and data science to develop DataCamp courses.
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