1. KNNL Problem 2.26 (This is a continuation of the analysis you did for HW2). For
_
part c, you need to define the variable Yˆi  Y . If you are using SAS, first generate
the predicted and residual values using Proc Reg and the output out= statement.
Assuming you name the predicted values pred and the residuals resid in the data set
a2 and you compute the sample mean of Y (YBAR), here is how you do the rest:
data a2; set a2;
resid1 = pred - YBAR;
symbol1 v=circle; symbol2 v=plus;
proc gplot;
plot resid*time resid1*time / overlay vref=0;
run;
2. KNNL Problem 2.31
3. Given that
R
2
= SSR/SSTO, it can be shown that
you have 28 cases and an
slope is equal to zero?
R
2
R
2
/(1 −
R
2
) = SSR/SSE. If
= 0.35, what is the F statistic for the test that the
For the next three problems use the grade point average data described in the text with KNNL
problem 1.19.
4. Describe the distribution of the explanatory variable. Show the plots and output that were
helpful in learning about this variable.
5. Run the linear regression to predict GPA from the entrance test score and obtain the residuals
(do not include a list of the residuals in your solution).
a. Verify that the sum of the residuals is zero by running PROC UNIVARIATE with the
output from the regression.
b. Plot the residuals versus the explanatory variable and briefly describe the plot noting any
unusual patterns or points.
c. Plot the residuals versus the order in which the data appear in the data file and briefly
describe the plot noting any unusual patterns or points.
d. Examine the distribution of the residuals by getting a histogram and a normal probability
plot of the residuals by using the histogram and qqplot statements in PROC
UNIVARIATE. What do you conclude?