1. For discussion during lecture #6:
From the text: Exercise 2.3, Exercise 3.3, Exercise 4.4, Exercise 5.5, Problem 6.1, Exercise 7.2
2. For discussion during lecture #14:
From the text: Problem 8.3, Problem 10.1, Exercise 11.4.3. For discussion during lecture #21 :
From the text Problem 12.2, Exercise 13.2, Exercise 13.3, Exercise 14.4,
Problem 16.8.
For Exercise 13.2 calculate the efficiency of using an RCB design.
4. For discussion during lecture #30 :
From the text Exercise 15.2, Problem 15.1b, Problem 15.4.
Analyze the data from Problem 16.8 as if the split-plot factor were a
repeated measure. Use the downweighted split-plot degrees of freedom method, the
method of modeling covariance matrices, and the random coefficient method. In
SAS the first method requires the data in multivariate format, which is
available here.
5. For discussion during lecture #38 :
i) Construct a 25-2 fractional factorial design. Use I=ABD=-CDE as the
defining relation. What is the resolution of the design? Write out the alias
structure for main effects and two-way interactions.
ii) Problem 18.6. (Find an embedded 24 factorial design. Find the 15
terms in the defining relation, then refer to the aliases when interpreting your
results.)
iii) For the linked data file, create a scatterplot of log sales and log
assets, using symbols or colors to label the sectors that data are from. Then
conduct an analysis of covariance to test for different in log sales by sector
after adjusting for log assets. Calculate the adjusted means for log sales by
sector, and calculate the adjusted mean of log sales for the HiTech by hand to
check the adjusted mean calculation. Test the parallelism hypothesis for the ANCOVA.
Assess the normality and constant variance assumptions for the ANCOVA model.
iv) Problems 18.1a, 18.2d, and 18.2h