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Course

Intermediate Regression in R

IntermediateSkill Level

4.7+

Updated 11/2025

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

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RProbability & Statistics4 hr14 videos50 Exercises4,150 XP34,591Statement 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. 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.

Prerequisites

Introduction to Regression in R

1

Parallel Slopes

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

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.

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

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

4

Multiple Logistic 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

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Intermediate Regression in R

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FAQs

What does parallel slopes regression mean in this course?

Parallel slopes models combine one numeric and one categorical explanatory variable, producing regression lines with the same slope but different intercepts for each category.

What R packages will I use in this course?

You will use dplyr for data manipulation and ggplot2 for visualization, building on tidyverse skills from earlier prerequisite courses to explore regression models.

Does this course cover logistic regression with multiple variables?

Yes. The final chapter extends logistic regression to multiple explanatory variables, explains the logistic distribution, and guides you through implementing your own logistic regression algorithm.

What real-world datasets are used for practice?

You will explore Taiwan house prices and customer churn modeling data to build regression models with multiple explanatory variables and interaction terms.

What is Simpson's Paradox, and is it addressed here?

Simpson's Paradox is when a trend in grouped data reverses when the groups are combined. The interactions chapter explains how it arises and how multiple explanatory variables can reveal it.

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