DataCamp

Adding trend lines from linear regression models

The low-level plot function abline() adds a straight line to an existing plot. This line is specified by an intercept parameter a and a slope parameter b, and the simplest way to set these parameters is directly. For example, the command abline(a = 0, b = 1) adds an equality reference line with zero intercept and unit (i.e. 1) slope: points for which y = x fall on this reference line, while points with y > x fall above it, and points with y < x fall below it.

An alternative way of specifying these parameters is through a linear regression model that determines them from data. One common application is to generate a scatterplot of y versus x, then fit a linear model that predicts y from x, and finally call abline() to add this best fit line to the plot.

This exercise asks you to do this for the Gas versus Temp data from the whiteside data frame in the MASS package. The standard R function that fits linear regression models is lm(), which supports the formula interface. Thus, to fit a linear model that predicts y from x in the data frame df, the call would be lm(y ~ x, data = df). This call returns a linear model object, which can then be passed as an argument to the abline() function to draw the desired line on our plot.

Use the lm() function to create linear_model, a linear regression model that predicts Gas from Temp from the whiteside data frame.
Generate a scatterplot of Gas vs. Temp.
Using the abline() function, add a dashed reference line (set line type to 2) that shows the predictions of linear_model.

Take Hint (-30 XP)

Exercise

Adding trend lines from linear regression models

Instructions