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 models

Assumptions of linear models

Refresher on fitting linear models

Poisson regression

Fitting a Poisson regression in R

Comparing linear and Poisson regression

Intercepts-comparisons versus means

Basic lm() functions with glm()

Applying summary(), print(), and tidy() to glm

Extracting coefficients from glm()

Predicting with glm()

GLMs, an extension of your regression toolbox

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

Overview of logistic regression

Fitting a logistic regression

Examining & interpreting logistic regression outputs

Bernoulli versus binomial distribution

Bernoulli versus binomial

Simulating binary data

Long-form logistic regression input

Wide-form input logistic regression

Comparing logistic regression outputs

Link functions-Probit compared to logit

Probit versus logit

Fitting probits and logits

Simulating a logit

Simulating a probit

Logistic Regression

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

Poisson regression coefficients

Poisson link

lm vs. Poisson coefficients

Plotting Poisson regression

Poisson regression plotting

Understanding output from logistic regression

Understanding odds ratios

Extracting and interpreting odds-ratios

Odds-ratios & confidence intervals in the Tidyverse

ggplot2 and binomial regression

Default trend lines

Methods for trend lines

Comparing probits and logits

Interpreting and visualizing GLMs 

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

Multiple logistic regression

Fitting a multiple logistic regression

Building two models

Comparing regression outputs

Comparing variable order

Formulas in R

Multiple slopes

Intercepts

Multiple intercepts

Assumptions of multiple logistic regression

Simpson's paradox

Non-linear logistic regression

Conclusion

Multiple regression with GLMs

Bus Commuter dataset

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.

Intermediate Regression in R

Learn to fit model binomial data with logistic regression to count data with Poisson regression, and how to understand and plot the results with ggplot2.

Generalized Linear Models in R

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

Instructor

Link functions- Probit compared to logit

Why link functions?

Why probit?

What is a probit?

Probit equation

Probit function

How does a probit compare to a logit?

Fitting a probit in R

Simulate with probit

Simulate with logit

When to use probit vs logit?

Let's practice!

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Logistic Regression - Link functions-Probit compared to logit

Link functions-Probit compared to logit

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