A obtain the estimated ridge standardized regression


Refer to Patient satisfaction Problem 6.15.

a. Obtain the estimated ridge standardized regression coefficients, variance inflation factors, and Rfor the following biasing constants: c = .000, .005, .01, .02, .03, .04, .05.

b. Make a ridge trace plot for the given c values. Do the ridge regression coefficients exhibit substantial changes near c = O?

c. Suggest a reasonable value for the biasing constant c based on the ridge trace, the VIF values, and R2.

d. Transform the estimated standardized regression coefficients selected in part (c) back to the original variables and obtain the fitted values for the 46 cases. How similar are these fitted values to those obtained with the ordinary least squares fit in Problem 6.15c?

Problem 6.15

Patient satisfaction. A hospital administrator wished to study the l-elation between patient satisfaction (Y) and patient's age (X I, in years), severity of illness (X2, an index), and anxiety level (X3 an index). The administrator randomly selected 46 patients and collected the data presented below, where larger values of Y, X2 , and X3 are, respectively, associated with more satisfaction, increased severity of illness, and more anxiety.

a. Prepare a stem-and-leaf plot for each of the predictor variables. Are any noteworthy features revealed by these plots?

b. Obtain the scatter plot matrix and the correlation matrix. Interpret these and state your principal findings.

c. Fit regression model (6.5) for three predictor variables to the data and state the estimated regression function. How is b2 interpreted here?

d. Obtain the residuals and prepare a box plot of the residuals. Do there appear to be any outliers?

e. Plot the residuals against , each of the predictor variables, and each two-factor interaction term on separate graphs. Also prepare a normal probability plot. Interpret your plots and summarize your findings.

f. Can you conduct a formal test for lack of fit here?

g. Conduct the Breusch-Pagan test for constancy of the error variance, assuming log σi2 =

 State the alternatives, decision rule, and conclusion.

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