After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
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.
Prerequisites
Curriculum
Course outline
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.
- Limitations of linear models50 XP
- Assumptions of linear models50 XP
- Refresher on fitting linear models100 XP
- Poisson regression50 XP
- Fitting a Poisson regression in R100 XP
- Comparing linear and Poisson regression100 XP
- Intercepts-comparisons versus means100 XP
- Basic lm() functions with glm()50 XP
- Applying summary(), print(), and tidy() to glm100 XP
- Extracting coefficients from glm()100 XP
- Predicting with glm()100 XP
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.
R
Generalized Linear Models in R
Course
Complete
