Skip to main content

Generalized Linear Models in R

The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.

Start Course for Free
4 Hours14 Videos51 Exercises14,156 Learners4050 XP

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Loved by learners at thousands of companies

Course Description

Linear regression serves as a workhorse of statistics, but cannot handle some types of complex data. A generalized linear model (GLM) expands upon linear regression to include non-normal distributions including binomial and count data. Throughout this course, you will expand your data science toolkit to include GLMs in R. As part of learning about GLMs, you will learn how to fit model binomial data with logistic regression and count data with Poisson regression. You will also learn how to understand these results and plot them with ggplot2.
  1. 1

    GLMs, an extension of your regression toolbox

    Free

    This chapter teaches you how generalized linear models are an extension of other models in your data science toolbox. The chapter also uses Poisson regression to introduce generalize linear models.

    Play Chapter Now
    Limitations of linear models
    50 xp
    Assumptions of linear models
    50 xp
    Refresher on fitting linear models
    100 xp
    Poisson regression
    50 xp
    Fitting a Poisson regression in R
    100 xp
    Comparing linear and Poisson regression
    100 xp
    Intercepts-comparisons versus means
    100 xp
    Basic lm() functions with glm()
    50 xp
    Applying summary(), print(), and tidy() to glm
    100 xp
    Extracting coefficients from glm()
    100 xp
    Predicting with glm()
    100 xp
  2. 2

    Logistic Regression

    This chapter covers running a logistic regression and examining the model outputs.

    Play Chapter Now
    Overview of logistic regression
    50 xp
    Fitting a logistic regression
    100 xp
    Examining & interpreting logistic regression outputs
    100 xp
    Bernoulli versus binomial distribution
    50 xp
    Bernoulli versus binomial
    50 xp
    Simulating binary data
    100 xp
    Long-form logistic regression input
    100 xp
    Wide-form input logistic regression
    100 xp
    Comparing logistic regression outputs
    50 xp
    Link functions-Probit compared to logit
    50 xp
    Probit versus logit
    50 xp
    Fitting probits and logits
    100 xp
    Simulating a logit
    100 xp
    Simulating a probit
    100 xp
  3. 3

    Interpreting and visualizing GLMs

    This chapter teaches you about interpreting GLM coefficients and plotting GLMs using ggplot2.

    Play Chapter Now
    Poisson regression coefficients
    50 xp
    Poisson link
    50 xp
    lm vs. Poisson coefficients
    100 xp
    Plotting Poisson regression
    50 xp
    Poisson regression plotting
    100 xp
    Understanding output from logistic regression
    50 xp
    Understanding odds ratios
    50 xp
    Extracting and interpreting odds-ratios
    100 xp
    Odds-ratios & confidence intervals in the Tidyverse
    100 xp
    ggplot2 and binomial regression
    50 xp
    Default trend lines
    100 xp
    Methods for trend lines
    100 xp
    Comparing probits and logits
    100 xp
  4. 4

    Multiple regression with GLMs

    In this chapter, you will learn how to do multiple regression with GLMs in R.

    Play Chapter Now
    Multiple logistic regression
    50 xp
    Fitting a multiple logistic regression
    100 xp
    Building two models
    100 xp
    Comparing regression outputs
    50 xp
    Comparing variable order
    100 xp
    Formulas in R
    50 xp
    Multiple slopes
    100 xp
    Intercepts
    100 xp
    Multiple intercepts
    100 xp
    Assumptions of multiple logistic regression
    50 xp
    Simpson's paradox
    100 xp
    Non-linear logistic regression
    100 xp
    Conclusion
    50 xp

Datasets

Bus Commuter dataset

Collaborators

dcamposliz
David Campos
chester
Chester Ismay
shoninouye
Shon Inouye

Prerequisites

Intermediate Regression in R
Richard Erickson Headshot

Richard Erickson

Data Scientist

Richard helps people to experience and understand their increasingly numerical world. For his day job he develops new quantitative methods for monitoring and controlling invasive species as well as helping other scientists analyze and understand their data. He has worked on diverse datasets ranging from continent wide species distributions to pesticides in playa wetlands. After hours, he teaches SCUBA Diving as a NAUI Instructor. He has been a member of "UserR" since 2007.
See More

What do other learners have to say?

    Join over 10 million learners and start Generalized Linear Models in R today!

    Create Your Free Account

    GoogleLinkedInFacebook

    or

    By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.