## Multiple linear regression in R

In a previous blog, we applied simple linear regression to an interesting problem: how well does a measure of wine density account for alcohol content. This was considered simple linear regression because we had one outcome variable (alcohol content) and one predictor variable (wine density). We can extend this approach to have more than one predictor. Specifically, we can use

## Simple linear regression in R

In statistics, we often want to fit a statistical model to be able to make broader generalizations. An important type of statistical model is linear regression, where we predict the linear relationship between an outcome variable and a predictor variable. In this post we will learn how to perform a simple linear regression in R. See our previous post for

## Verify if data are normally distributed in R: part 3

In the first and second post of this series, we learned how to graph our data using histograms and Q-Q plots to see whether it is normally distributed, and quantify the shape of the distribution by considering skew and kurtosis. In this, the final post in this series, we will learn to use the Shapiro-Wilk test to determine whether data