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

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

Parallel Slopes

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.

Models for each category

One model per category

Predicting multiple models

Visualizing multiple models

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

Interactions

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.

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

Multiple Linear 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.

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

Congratulations!

Multiple Logistic Regression

Ad conversion

Customer churn

Taiwan real estate

Fish measurement data

eBay auctions

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.

Introduction to Regression with statsmodels in Python

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

Intermediate Regression with statsmodels in Python

Principes de la statistique  en Python

Content Developer at DataCamp

What is an interaction?

Specifying interactions

Running the model

Easier to understand coefficients

Familiar numbers

Let's practice!

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Interactions - One model with an interaction

One model with an interaction

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