Data Carpentryâ€™s aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. This lesson was designed for researchers interested in working with public health data in R, but may be of interest to researchers in other fields as well.

This lesson provides an introduction to statistical concepts commonly used for linear modelling. It is a prerequisite for the other lessons in the statistics for public health curriculum. The lesson covers commonly used data distributions, plotting data distributions, hypothesis testing, correlations, predictive relationships between variables, how to specify a model in R and the importance of considering model assumptions.

## Getting started

To get started, see the instructions in the Setup page. There you will learn how to obtain the data and packages used in this lesson.

PrerequisitesThis lesson does not require a formal background in statistics.

This lesson requires:

- Working copies of R and RStudio. See here for installation instructions.
- An understanding of how to use the Tidyverse packages to summarise and manipulate data in RStudio. See these episodes on data handling and data manipulation.
- An understanding of how to use the ggplot2 package to plot data in RStudio. See this episode on data visualisation.