Pular para o conteúdo principal

curso

Inference for Linear Regression in R

Avançado

Updated 12/2024

In this course you'll learn how to perform inference using linear models.

Iniciar curso gratuitamente

Incluído gratuitamentePremium or Teams

RProbabilidade e estatística4 horas15 vídeos59 exercícios4,650 XP14,185Declaração de Realização

Crie sua conta gratuita

Google LinkedIn Facebook

ou

Ao continuar, você aceita nossos Termos de Uso, nossa Política de Privacidade e que seus dados são armazenados nos EUA.

Treinar 2 ou mais pessoas?

Tentar DataCamp for Business

Amado por alunos de milhares de empresas

Descrição do curso

Previously, you learned the fundamentals of both statistical inference and linear models; now, the next step is to put them together. This course gives you a chance to think about how different samples can produce different linear models, where your goal is to understand the underlying population model. From the estimated linear model, you will learn how to create interval estimates for the effect size as well as how to determine if the effect is significant. Prediction intervals for the response variable will be contrasted with estimates of the average response. Throughout the course, you'll gain more practice with the dplyr and ggplot2 packages, and you will learn about the broom package for tidying models; all three packages are invaluable in data science.

Pré-requisitos

Foundations of Inference in R Intermediate Regression in R

1

Inferential ideas

Iniciar capítulo

Variability in regression lines

Regression output: example I

First random sample, second random sample

Superimpose lines

Research question

Regression hypothesis

Variability of coefficients

Original population - change sample size

Hypothetical population - less variability around the line

Hypothetical population - less variability in x direction

What changes the variability of the coefficients?

2

Simulation-based inference for the slope parameter

Iniciar capítulo

Simulation-based Inference

Null sampling distribution of the slope

SE of the slope

Inference on slope

Simulation-based CI for slope

Bootstrapping the data

SE method - bootstrap CI for slope

Percentile method - bootstrap CI for slope

Inference from randomization and bootstrapped distributions

3

t-Based Inference For the Slope Parameter

Iniciar capítulo

Mathematical approximation

How do the theoretical results play a role?

t-statistic

Working with R-output (1)

Working with R-output (2)

Comparing randomization inference and t-inference

Intervals in regression

CI using t-theory

Comparing randomization CIs and t-based CIs

Different types of intervals

Confidence intervals for the average response at specific values

Confidence intervals for the average response for all observations

Prediction intervals for the individual response

4

Technical Conditions in linear regression

Iniciar capítulo

Technical conditions for linear regression

Violation of LINE conditions (1)

Violation of LINE conditions (2)

Using residuals (1)

Using residuals (2)

Why do we need the LINE assumptions?

Effect of an outlier

Estimation with and without outlier

Inference with and without outlier (t-test)

Inference with and without outlier (randomization)

Moving forward when model assumptions are violated

Adjusting for non-linear relationship

Adjusting for non-constant errors

Adjusting for non-normal errors

5

Building on Inference in Simple Linear Regression

Iniciar capítulo

Inference on transformed variables

Transformed model

Interpreting transformed coefficients

Multicollinearity

LA Homes, multicollinearity (1)

LA Homes, multicollinearity (2)

LA Homes, multicollinearity (3)

Multiple linear regression

Inference on coefficients

Interpreting coefficients

Inference for Linear Regression in R

Curso
Completo

Declaração de Realização Earn

Adicione esta credencial ao seu perfil, currículo ou currículo do LinkedIn
Compartilhe nas redes sociais e em sua avaliação de desempenho

Incluído comPremium or Teams

Inscreva-se agora

Junte-se a mais 15 milhões de alunos e comece Inference for Linear Regression in R Hoje!

Crie sua conta gratuita

Google LinkedIn Facebook

ou

Ao continuar, você aceita nossos Termos de Uso, nossa Política de Privacidade e que seus dados são armazenados nos EUA.