R – Basic package

Content

Introduction to R • Introduction to modern statistics • Linear regression • ANOVA • Advanced regression • Mixed models • Missing data

4 course days - SEK 22.500

R 1 - Introduction to R and to modern statistics – 2 days

This course helps you get started with R. We’ll cover the basics of R, ranging from importing and handling data to visualisation. You’ll learn about two fundamental tools in statistical analysis: hypothesis tests and confidence intervals. We’ll also discuss important concepts like p-values, power, and sample size calculcations.

The popular tidyverse package is used for filtering, cleaning, and preparing data for analysis. The powerful plotting capabilities of the ggplot2 package are also covered. Both basic statistical concepts and fundamental topics in R programming are discussed. This course is a great fit if you’re curious about R, or already know that you want to use its many tools for advanced data analysis. Classical statistical tests like the t-test, nonparametric tests and the chi-squared test are covered, along with modern computer-intensive methods like the bootstrap. The latter allows us to obtain p-values and confidence intervals without many of the constraints of traditional methods (such as requiring that the data follow a normal distribution), bringing your statistical toolbox up to the 21st century.

R 2 - Linear regression, ANOVA & advanced regression models – 2 days

This course provides you with a solid understanding of modern linear regression and ANOVA models. It also covers some common but advanced regression models for dealing with categorical data and repeated measurements.

We will have a closer look at how these models work and how R can be used to build, visualise, and interpret such models. We will use modern techniques like the bootstrap and permutation tests, to obtain confidence intervals and p-values without having to assume a normal distribution for your data. We will cover non-linear regression models like logistic regression and Poisson regression, where the response variable can be either binary (yes/no), counts, or prevalence. Mixed models are used to analyse data with repeated measurements on the same subjects. We also learn about methods for dealing with missing data.