1. Problems of Tutorial 9
(Problem 4.12, Page 120) Download the “Data for Exercise ” from the class website. The data consist of 1 response variable and 6 predictor variables. Consider fitting a linear model relating Y to all predictor variables.
a). What least squares assumptions (if any) seem to be violated?
b). Compute
c). Construct the index plots of as well as the Potential-Residual plot.
d). Identify all unusual observations in the data and classify each according to type (outlier, high leverage observation, influential observation)
(Problem 4.14, Page 120). Consider fitting the model to the same data set as that of the first 2 problems. Now let u be the residuals obtained from regressing Y on X1 and X2, and v be the residuals obtained from regressing X3 on X1 and X2. Show (or verify using the above data set as an example) that
a).
b). The standard error of is
5/5/2019
ST3131, Tutorial 9

2. (4.4, page 116) In an attempt to find unusual points in a regression data set, a data analyst examines the P-R plot as in the right-hand side. Classify each of the unusual points in this plot according to type. 11 7 18
4. Download the “Data for Exercise ” from the class website. The data consist of 1 response and 6 predictor variables. Suppose we fit a linear model relating Y to the first three X-variables. Justify your answer to each of the following questions with the appropriate added-variable plot:
a). Should we add X4 to the above model? If yes, keep X4 in the model.
b). Should we add X5 to the above model? If yes, keep X5 in the model.
c). Should we add X6 to the above model?
d). What model would you recommend as the best possible description of Y? Use the above results and /or perform additional analysis if needed.
5/5/2019
ST3131, Tutorial 9