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Intermediate Regression with statsmodels in Python

Intermediate

4.8+

Updated 05/2025

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

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PythonProbability & Statistics4 hours14 videos52 Exercises4,300 XP12,367Statement of Accomplishment

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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.

Prerequisites

Introduction to Regression with statsmodels in Python

1

Parallel Slopes

Parallel slopes linear regression

Fitting a parallel slopes linear regression

Interpreting parallel slopes coefficients

Visualizing each explanatory variable

Visualizing parallel slopes

Predicting parallel slopes

Predicting with a parallel slopes model

Visualizing parallel slopes model predictions

Manually calculating predictions

Assessing model performance

Comparing coefficients of determination

Comparing residual standard error

2

Interactions

Models for each category

One model per category

Predicting multiple models

Visualizing multiple models

Assessing model performance

One model with an interaction

Specifying an interaction

Interactions with understandable coeffs

Making predictions with interactions

Predicting with interactions

Manually calculating predictions with interactions

Simpson's Paradox

Modeling eBay auctions

Modeling each auction type

3

Multiple Linear Regression

Two numeric explanatory variables

Interactive 3D scatter plot

Visualizing three numeric variables

Modeling two numeric explanatory variables

Visualizing two numeric explanatory variables

Including an interaction

More than two explanatory variables

Visualizing many variables

Different levels of interaction

Predicting again

How linear regression works

The sum of squares

Linear regression algorithm

4

Multiple Logistic Regression

Multiple logistic regression

Logistic regression with two explanatory variables

Logistic regression prediction

Visualizing multiple explanatory variables

Confusion matrix

The logistic distribution

Cumulative distribution function

Inverse cumulative distribution function

Logistic distribution parameters

How logistic regression works

Likelihood & log-likelihood

Logistic regression algorithm

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Meriem

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very hands on, great way to learn and apply skills

Refael

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Wahyu J. R.

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Meriem

Tatevik

"very hands on, great way to learn and apply skills"

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