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

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 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.

Two numeric explanatory variables

3D visualizations

Modeling 2 numeric explanatory variables

Including an interaction

More than 2 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. Understand the logistic distribution, which underpins this form of regression. Finally, implement your own logistic regression algorithm.

Multiple logistic regression

Visualizing multiple explanatory variables

Logistic regression with 2 explanatory variables

Logistic regression prediction

Confusion matrix

The logistic distribution

Cumulative distribution function

Inverse cumulative distribution function

binomial family argument

Logistic distribution parameters

How logistic regression works

Likelihood & log-likelihood

Logistic regression algorithm

Congratulations

Multiple Logistic Regression

Taiwan real estate prices

eBay Palm Pilot auctions

Bank churn

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.

Introduction to Regression in R

Discover how to include multiple explanatory variables in a model, how interactions affect predictions, and how linear and logistic regression work in R.

Intermediate Regression in R

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

Logistic distribution parameters

Intermediate Regression in R

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