STAT 704 Fall 2009
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Homework 7
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Do the following problems from the textbook:

10.2, 10.3, 10.8, 10.11(a,b,d), 10.12(a,b,d), 10.26.
11.1, 11.2, 11.6(a,c,d,e,f), 11.19
In addition:
**Do this alteration of problem 11.9:**
 For the cosmetics data in problem 10.13:
(a) Fit an ordinary least squares regression of Y 
against X1, X2, and X3.  Calculate the VIFs.  What 
do these tell you?
(b) Load the MASS package in R and fit the ridge regression 
of Y against X1, X2, and X3, using a biasing constant of lambda=0.1.  
Write the fitted ridge regression equation.  How do the coefficient 
estimates compare to those of the fitted OLS equation?
(c) How do the SSE's for the two models compare?
***Do this alteration of problem 11.12:**
(a) Fit the OLS regression of weight against height and 
use the influence measures to detect any possible 
influential observations.
(b) Load the MASS package in R and use R's rlm()
function to fit Huber's robust regression of weight 
against height. Write the fitted robust regression equation.  
How do the coefficient estimates compare to those
of the fitted OLS equation?
13.1, 13.3, 13.4, 13.10, 13.11(a,b), 13.12, 13.13, 13.14


NOTE: For 10.11(a) & 10.12(a), you may find the 
internally studentized residuals and compare 
their absolute values to 2.5 instead of doing the
Bonferroni outlier test.  For 10.11(d) and 10.12(d),
don't worry about the DFBETAS.

NOTE:  On page 359 and page 433, the book's notation sigma^2{.} is
equivalent to: var{.}

NOTE:  For 13.12, you may make your conclusion about the requested 
hypothesis test by looking at the appropriate CI.

The "Computer Learning", "Cosmetics Sales", "Weight/Height",
"Patient Satisfaction", "Enzyme Kinetics" and "Drug Responsiveness" 
data can be found on the course web page.

Please write your answers neatly and clearly!