Interactive Course

Generalized Linear Models in R

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

  • 4 hours
  • 14 Videos
  • 51 Exercises
  • 3,921 Participants
  • 4,050 XP

Loved by learners at thousands of top companies:

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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.

  2. Logistic Regression

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

  3. Interpreting and visualizing GLMs

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

  4. Multiple regression with GLMs

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

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Richard Erickson
Richard Erickson

Quantitative Ecologist

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 "UserR" since 2007.

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Collaborators
  • Chester Ismay

    Chester Ismay

  • David Campos

    David Campos

  • Shon Inouye

    Shon Inouye

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