In a linear trend model, one constructs a linear regression least squares line by running an lm() regression through the data points. These models are good for initially exploring trends visually. We can take advantage of our lm() function, which is available in base R, in order to specifically calculate the slope of the trend line.
For example, the first 14 rows show the data for the entire population group (ALL AGES). We can run a regression on Not.Covered.Pct for each of the years numbered 1-14 and see that the coefficient for the Year.1 variable is positive, indicating that there is a linear increase in the non-coverage percentage as ime advances.
We can also the coeficient output by itself, by wrapping the lm() function within a coef() function.
After running the regression using the lm() function, we can subsequently use the coef function to specifically...
