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In the next blog post, we will look again at regression. By the way – lm stands for “linear model”.įinally, we can add a best fit line (regression line) to our plot by adding the following text at the command line: abline(98.0054, 0.9528)Īnother line of syntax that will plot the regression line is: abline(lm(height ~ bodymass)) We see that the intercept is 98.0054 and the slope is 0.9528. Now let’s perform a linear regression using lm() on the two variables by adding the following text at the command line: lm(height ~ bodymass) Call: In the above code, the syntax pch = 16 creates solid dots, while cex = 1.3 creates dots that are 1.3 times bigger than the default (where cex = 1). Copy and paste the following code into the R workspace: plot(bodymass, height, pch = 16, cex = 1.3, col = "blue", main = "HEIGHT PLOTTED AGAINST BODY MASS", xlab = "BODY MASS (kg)", ylab = "HEIGHT (cm)") We can enhance this plot using various arguments within the plot() command. We can now create a simple plot of the two variables as follows: plot(bodymass, height) Copy and paste the following code to the R command line to create the bodymass variable. Now let’s take bodymass to be a variable that describes the masses (in kg) of the same ten people. Copy and paste the following code to the R command line to create this variable. We take height to be a variable that describes the heights (in cm) of ten people. Today let’s re-create two variables and see how to plot them and include a regression line.