# Intermediate Regression with statsmodels in Python

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

4 Hours14 Videos52 Exercises
4300 XP

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).

## 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.

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
2. 2

### Interactions

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.

3. 3

### Multiple Linear Regression

See how modeling and linear regression make 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.

4. 4

### Multiple Logistic Regression

Extend your logistic regression skills to multiple explanatory variables. You’ll also learn about logistic distribution, which underpins this form of regression, before implementing your own logistic regression algorithm.

Datasets

Ad conversionCustomer churnTaiwan real estateFish measurement dataeBay auctions

Collaborators Richie Cotton Maggie Matsui Amy Peterson #### 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.

## What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden