Linear regression is the first part in a bunch of videos I’m going to do about General Linear Models.

I also made a companion StatQuest that shows how to do linear regression in R:

Here’s the code from the video if you want to try it out yourself:

## Here's the data from the example:
mouse.data <- data.frame(
weight=c(0.9, 1.8, 2.4, 3.5, 3.9, 4.4, 5.1, 5.6, 6.3),
size=c(1.4, 2.6, 1.0, 3.7, 5.5, 3.2, 3.0, 4.9, 6.3))
mouse.data # print the data to the screen in a nice format
## plot a x/y scatter plot with the data
plot(mouse.data$weight, mouse.data$size)
## create a "linear model" - that is, do the regression
mouse.regression <- lm(size ~ weight, data=mouse.data)
## generate a summary of the regression
summary(mouse.regression)
## add the regression line to our x/y scatter plot
abline(mouse.regression, col="blue")

[…] via StatQuest: Linear Regression (aka GLMs, part 1) […]

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