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Solution of Assignment #7
Instructor: A. Simchi
Question #1:
(a) The least-square line of SBP (Y ) on QUET (X) for Smokers and nonsmokers
are:
Non smokers : SBP = 49.312 + 26.303 QU ET
Smokers : SBP = 79.255 + 20.118 QU ET
(b) Let The single multiple mode
Solution of Assignment #4
Instructor: A. Simchi
Question #1:
(a) The least-square estimate of the regression line when Y regressed on X1 is:
Y = 70.42020 + 227.09370 X1
Based on the computer output on pages 1 and 2, we have R2 = 0.9194 and
2
rY X1 = 0.958
Solution of Assignment #2
Instructor: A. Simchi
Question #1:
(a) The least-square estimate of the regression line when Y regressed on X is:
Y = 1.69956 + 0.83991 X
The change in the mean response when X increases by one unit is just 1 .
Therefore, the est
Solution of Assignment #6
Instructor: A. Simchi
Question #1:
(a) The plot of Y versus X is given on page 1 of SAS output. It is clear that a
straight line model is not adequate. By looking at the graph, it seems that the
1
transformation X or ln(X) is mor
Stat 378/502 Al Midterm Exam Name: S 911%.; i 0 \n Kc at
October 28, 2005 Student No: Q 1; 4; 3 g 53
Page 2 of 4
1. Consider the following model:
Y=0+B1X1+ZX2+B3X3+E
(1 m q Hap) What is the Adjusted R2? {,LD
R9. MSW , I 31%.212/33 : a, 1?;
Mia r ml 403.
-10 0 10 20 30 40 50 60
Degreedays
Gas = 1.0892108 + 0.188999 Deree-days
RSquare 0.99055
FlSquare Adj 0.989875
Root Mean Square Error 0.338928
Mean of Heeponse 5.30625
Observations (or Sum Wgts) 16
Source DF Sum of Squares Mean Square F Flaiio
M
Ken's Comments Based on a Light Reading of Labs #6 1. The lab asked for plots of residuals vs. each predictor in the final model. Most people included plots of leverage residuals vs. each predictor in the final model. Leverage residuals and residuals are