Introduction to Regression with R

Introduction to Regression with R#

Overview#

Regression analysis is a fundamental statistical technique used to model the relationship between multiple variables. It is a very flexible tool that can handle a range of different data types (continuous, binary or categorical) and address both complex and simple research questions. Here we look at how to fit a range of regression models with R, how to interpret the output and the link between regression and other common statistical tools.

Course Objectives#

  • Understand the types of questions regression can be used to answer.

  • Be able to fit a linear regression model with multiple predictor variables.

  • Be able to fit a logistic regression model with multiple predictor variables.

  • Be able to extract and summarise the results from a range of regression models.

  • Be able to design a regression model appropriate for addressing a specific research question.

Pre-requisite Knowledge#

Learners are expected to already be familiar with the basics of R, such as how to load a dataset from a local file and manipulate variables.

Install necessary R packages#

There are three packages need for this workshop. The first two (devtools & learnr) are available from CRAN. The third is a package we have developed with the course materials in and is available from GitHub (cfrrRTutorials).

This code will install these three packages.

install.packages("devtools") 
install.packages("learnr") 
library(devtools)
devtools::install_github("ejh243/cfrr-r-tutorials")
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