# Course Generalized Linear Models in R

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

DescriptionChaptersExercisesInstructor

This online course about Generalized Linear Models in R covers a key part of what a future data analyst would require.

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.

Enroll now in this Generalized Linear Models in R course, and don’t miss the opportunity of learning with the best, as Richard Erickson is. With 56 enriching exercises, 14 videos, and an estimated time of 4 hours to successfully end up the course, you will become one of the best.

Requisites before you start
Chapter 1: GLMs, an extension of your regression toolbox
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.
Chapter 2: Logistic Regression
This chapter covers running a logistic regression and examining the model outputs.
Chapter 3: Interpreting and visualizing GLMs
This chapter teaches you about interpreting GLM coefficients and plotting GLMs using ggplot2.
Chapter 4: Multiple regression with GLMs
In this chapter, you will learn how to do multiple regression with GLMs in R.
Chapter 5:
Chapter 6:
Chapter 7:
Chapter 8:
Chapter 9:
Chapter 10:
Chapter 11:
Chapter 12:

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.

Collaborators

#R #Python #MachineLearning #BigData #DataAnalysis