Saltar al contenido principal

curso

Generalized Linear Models in R

Intermedio

Actualizado 1/2025

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

Comienza el curso gratis

Incluido de forma gratuitaPremium or Teams

RProbability & Statistics4 horas14 vídeos51 ejercicios4,050 XP19,307Declaración de cumplimiento

Crea Tu Cuenta Gratuita

Google LinkedIn Facebook

o

Al continuar, acepta nuestros Términos de uso, nuestra Política de privacidad y que sus datos se almacenan en los EE. UU.

¿Entrenar a 2 o más personas?

Probar DataCamp for Business

Preferido por estudiantes en miles de empresas

Descripción del curso

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.

Prerrequisitos

Intermediate Regression in R

1

GLMs, an extension of your regression toolbox

Iniciar capítulo

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()

2

Logistic Regression

Iniciar capítulo

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

3

Interpreting and visualizing GLMs

Iniciar capítulo

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

4

Multiple regression with GLMs

Iniciar capítulo

Multiple logistic regression

Fitting a multiple logistic regression

Building two models

Comparing regression outputs

Comparing variable order

Formulas in R

Multiple slopes

Multiple intercepts

Assumptions of multiple logistic regression

Simpson's paradox

Non-linear logistic regression

Generalized Linear Models in R

Curso
Completo

Obtener Declaración de Logro

Añade esta credencial a tu perfil, currículum vitae o CV de LinkedIn
Compártelo en las redes sociales y en tu evaluación de desempeño

Incluido conPremium or Teams

Inscríbete ahora

Únete a más 15 millones de estudiantes y empezar Generalized Linear Models in R ¡Hoy!

Crea Tu Cuenta Gratuita

Google LinkedIn Facebook

o

Al continuar, acepta nuestros Términos de uso, nuestra Política de privacidad y que sus datos se almacenan en los EE. UU.