# Regression - using Fitted Line Plot

```Regression - using Fitted Line Plot
There are two methods of doing regression –
using Stat &gt; Regression &gt; Fitted Line Plot
and
using Stat &gt; Regression &gt; Regression.
It is easier to read the output of Fitted Line Plot and that is adequate for our initial work in regression.
When we get to inference in regression, at the end of the course, we need more detailed output.
Our example is the voting data used in the scatterplot examples. We will find the regression equation
and the R2 to using the 1984 percentage voting Democratic as the response variable and the 1980
percentage voting Democratic as the explanatory variable.
I chose the appropriate response variable and explanatory variable and chose Linear for the type of
Regression Model. (Other types of regression are studied in more advanced courses.)
Here is the output.
Fitted Line Plot
84-Dem = 9.539 + 0.7489 80-Dem
90
S
R-Sq
80
5.31 020
64.0%
63.2%
84-Dem
70
60
50
40
30
20
20
30
40
50
60
70
80
80-Dem
In the language of our text, the regression equation is yˆ  9.539  0.7489 x and the value of the
coefficient of determination, r 2 , is 0.64 or 64%.
For now, ignore the value of S. We’ll use that when we start inference with regression in Chapter 24.
Also ignore the adjusted r 2 . That is beyond the scope of this course. (You may look it up in Wikipedia
by searching on “regression adjusted r-squared.”)
As mentioned before in the scatterplot documents, it is interesting to consider how strongly that upper
right-hand corner point affects the analysis. Keep reading on to where we discuss influential points.
```