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13. Regressions for Decision Making (20 points) The station manager of a local television station...

13. Regressions for Decision Making (20 points) The station manager of a local television station is interested in predicting

13. Regressions for Decision Making (20 points) The station manager of a local television station is interested in predicting the will watch in the viewing area. The explanatory variables are: age (n years years), and family size (number of family members in household). The multiple regression n predicting the amount of television (in hours) that people education (highest level obtained, in output from Excel is shown 0 6644 05598 R-Square of Estimate ANOVA Table 13.9682 5.6413 4.6561 0.3134 14.8564 0,0000 18 .683 050 213 1.170 0.200 0.050 0.0784 1.4389 0.2501 4.2426 5.1680 0.022 0.115 0.016 (X1) Family Size [X) 0.405 (a). Based on the estimation outputs, write down the function form between the dependent variable and (b). Interpret the result for each independent variable (ie., Age, Education, Family Size). (4 points) (c). Fora person of 40 years old, with MBA degree (Education-18 years), and from a family of 5 people, what is the predicted hours this person will watch the local television station based on your estimation? (3 points) (d). If you are the station manager, what strategy will you take to increase Write only one strategy which you think is the best.) (3 points) your viewership based on your analysis? (e). The manager also has data on each person's profession. They are: financial manager, accountant, operations manager, and sales associate. The manager wants to study if their professions have any impact on their TV watching behavior. A sampla of data is shown in the table below. Using the sample data, show how the manager should pperationalize the profession data and enter them into the regression? (6 points) (Show your work.) Person John Eric Alice Andy ordan Peggy operations manager operations manager sales associate sales associate
0 0
ReportAnswer #1

a)
y^ = 1.683 - 0.050 age + 0.213 Education + 0.405 Family Size

b)
when age increases by 1 unit, the amount of Television decrease by 0.05
when Education increases by 1 unit, the amount of Television increase by 0.213
when Family Size increases by 1 unit, the amount of Television increase by 0.405

c)
y^ = 1.683 - 0.050 age + 0.213 Education + 0.405 Family Size
= 1.683 - 0.050 *40 + 0.213 *18 + 0.405 *5
= 5.542

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