Page 108 - Applied Statistics with R
P. 108
108 CHAPTER 7. SIMPLE LINEAR REGRESSION
SSReg (Sum of Squares Regression) SSE (Sum of Squares Error)
10 10
0 0
-10 -10
y y
-20 -20
-30 -30
0 2 4 6 8 10 0 2 4 6 8 10
x x
SST (Sum of Squares Total) SST (Sum of Squares Total)
10 10
0 0
-10 -10
y y
-20 -20
-30 -30
0 2 4 6 8 10 0 2 4 6 8 10
x x
7.4 The lm Function
So far we have done regression by deriving the least squares estimates, then
writing simple R commands to perform the necessary calculations. Since this is
such a common task, this is functionality that is built directly into R via the
lm() command.
The lm() command is used to fit linear models which actually account for
a broader class of models than simple linear regression, but we will use SLR
as our first demonstration of lm(). The lm() function will be one of our most
commonly used tools, so you may want to take a look at the documentation by
using ?lm. You’ll notice there is a lot of information there, but we will start
with just the very basics. This is documentation you will want to return to
often.
