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-regressiecoëfficiënten

Poisson-koppeling

lm- versus Poisson-coëfficiënten

Poisson-regressie plotten

Uitvoer van logistische regressie begrijpen

Kansverhoudingen begrijpen

Oddsratio’s extraheren en interpreteren

Odds-ratio’s & betrouwbaarheidsintervallen in de Tidyverse

ggplot2 en binomiale regressie

Standaard trendlijnen

Methoden voor trendlijnen

Probits en logits vergelijken

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

Lineaire regressie is een werkpaard binnen de statistiek, maar kan sommige soorten complexe data niet aan. Een generalized linear model (GLM) breidt lineaire regressie uit met niet-normale verdelingen, waaronder binomiale en teldata. In deze cursus breid je je data science-gereedschapskist uit met GLM’s in R. Je leert binomiale data te modelleren met logistische regressie en teldata met Poisson-regressie. Ook leer je hoe je deze resultaten interpreteert en visualiseert met ggplot2.

Intermediate Regression in R

Leer binomiale data te modelleren met logistische regressie en teldata met Poisson-regressie.

Generalized Linear Models in R

De cursus Generalized Linear Model breidt je regressietoolbox uit met logistische en Poisson-regressie.

lm- versus Poisson-coëfficiënten

Generalized Linear Models in R

Praktische interactieve oefening