1. This problem uses an SPSS data set called HW5_1.sav. Market research was conducted for a national retail company to compare the relationship between sales and advertising during the warm Spring and Summer seasons as compared with the cool Fall and Winter seasons. See page 256 for the data table (note that Warm season = 0 and Cool season = 1).
   1. Identify a single regression model that uses the data for both warm and cool seasons and that defines straight-line models relating sales revenue ( Y ) to advertising expenditure ( X ) for each season. Fit this single regression model using SPSS and report the prediction equation ( Y regressed on X ) for each season (warm and cool).
   2. Test whether straight lines for cool and warm seasons coincide (use =0.05).
   3. Test H ₀ : "The lines are parallel" vs. H _A : "The lines are not parallel" (use =0.05).
   4. In light of your findings in b and c, interpret your findings (feel free to provide a plot of the two fitted straight lines if you feel that it will aid your interpretation).
2. This problem uses an SPSS data set called HW5_2.sav. A survey was conducted to examine the effects of pharmaceutical drug advertising on physician practices. Self-administered questionnaires were delivered to randomly selected physicians in various practice types: general practice, family practice, and internal medicine. Physicians reported on their attitude toward drug advertising. Likert scale measurements were used in the survey (1=strongly disagree and 5=strongly agree; the higher the score, the more favorably disposed the respondent is to advertising). See page 469 for the data.
   1. Test whether the physician types differ significantly in average attitude toward advertising. State your null hypothesis and the result of the test (use =0.05) (use either the GLM procedure or the one-way ANOVA procedure in SPSS).
   2. Using dummy variables, create an appropriate regression model for this situation (use either reference cell coding or effect coding, but not both!). Interpret the regression coefficients for your model and verify that the omnibus F value is the same in regression as when you conducted the ANOVA in part a).
   3. Use the Brown-Forsythe test to assess whether or not the assumption of homogeneity of variance is violated. Report and interpret your results.
   4. Use Tukey’s HSD test to locate differences between pairs of means.

Bonus: Do problem 18b in Kleinbaum et al . Chapter 17 (see page 471). You only need to do part b – provide the ANOVA table and show your work (assume customer type is a fixed-effects factor). Note that there are no raw data for this problem – you need to put the ANOVA table together using only the summary data provided. Do this by "hand and you must show your work. Also, do it using SPSS and please provide your code (i.e., commands).

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