~ SAS Language ~

banking.txt:

Age Education Income Balance 35.9 14.8 91033 38517 37.7 13.8 86748 40618 36.8 13.8 72245 35206 35.3 13.2 70639 33434 35.3 13.2 64879 28162 34.8 13.7 75591 36708 39.3 14.4 80615 38766 36.6 13.9 76507 34811 35.7 16.1 107935 41032 40.5 15.1 82557 41742 37.9 14.2 58294 29950 43.1 15.8 88041 51107 37.7 12.9 64597 34936 36 13.1 64894 32387 40.4 16.1 61091 32150 33.8 13.6 76771 37996 36.4 13.5 55609 24672 37.7 12.8 74091 37603 36.2 12.9 53713 26785 39.1 12.7 60262 32576 39.4 16.1 111548 56569 36.1 12.8 48600 26144 35.3 12.7 51419 24558 37.5 12.8 51182 23584 housesalex.txt

Region Type Price Cost M SF 348744 53000.00 M SF 274455 41000.00 M SF 277720 44650.00 M SF 307373 41292.00 M SF 271105 45000.00 M SF 262740 44900.00 M SF 175000 28000.00 M SF 201700 40940.00 M SF 283440 50900.00 M SF 185160 29000.00 M SF 323716 34500.00 M SF 281487 57285.00 M SF 184460 22300.00 M SF 289000 44000.00 M SF 410810 66500.00 M SF 184210 28000.00 M SF 223890 28000.00

A university career center collects information on the job status and starting salary of graduating seniors. Data recently collected over a two-year period included over 900 seniors who had found emplayment at the time of graduation. The information was used to model starting salary Y as a function of two qualitative independent variables: COLLEGE at four levels Business, Engineering, Liberal Arts, Nursing) and SEX (male and female) 1. Define the dummy variables to include college (use Business as your baseline) in a regression model for starting salary 2. Write down the general regression model relating starting salary Y to both college and sex. 3. How would your model change if students in Engineering have the same starting salary as students in Business? Show the final regression model. You will continue the analysis of the banking.txt dataset Mnalyze the residuals of the regression model you found in your previous assignment. Include the residual plots. Discuss your findings a) Conduct a global F-test for overall model adequacy. Write down the test hypotheses and test statistic and discuss conclusions. Include the relevant output. b) Copy and paste your FULL SAS code into the word document along with your answers. A national homebuilder builds single-family homes and condominium style townhouses. The file housesales.txt provides information on the selling price (PRICE), lot cost (COST), type of home HOME ISF-single family home or T-condominium style) and region of the country (REGION) M-Midwest, S-south) for closings during one month. a) b) c) Define the dummy variables for region and home (write them down here), and create them in Analyze the association between selling price and each individual attribute (cost, home and region) using appropriate statistics and graphs. Discuss your findings. Include the relevant output. Fit an adequate regression model for sales price as a function of lot cost, region of country, and type of home. Remove the terms that are not significant. The final model should only contain variables that are significantly associated with sale price. Write down the model equation. Include the relevant output. d) Conduct a global F-test for overall model adequacy. Write down the test hypotheses and test statistic and discuss conclusions. Include the relevant output. Analyze model residuals to check if assumptions on data are satisfied. Discuss your findings. Include the relevant output. Discuss what the regression model indicates for the relationship between price and home type i.e. interpret the coefficient values). e) g) Use the regression analysis to determine whether mean sale prices are different for the two regions? Explain. Copy and paste your FULL SAS code into the word document along with your answers. h) A university career center collects information on the job status and starting salary of graduating seniors. Data recently collected over a two-year period included over 900 seniors who had found emplayment at the time of graduation. The information was used to model starting salary Y as a function of two qualitative independent variables: COLLEGE at four levels Business, Engineering, Liberal Arts, Nursing) and SEX (male and female) 1. Define the dummy variables to include college (use Business as your baseline) in a regression model for starting salary 2. Write down the general regression model relating starting salary Y to both college and sex. 3. How would your model change if students in Engineering have the same starting salary as students in Business? Show the final regression model. You will continue the analysis of the banking.txt dataset Mnalyze the residuals of the regression model you found in your previous assignment. Include the residual plots. Discuss your findings a) Conduct a global F-test for overall model adequacy. Write down the test hypotheses and test statistic and discuss conclusions. Include the relevant output. b) Copy and paste your FULL SAS code into the word document along with your answers. A national homebuilder builds single-family homes and condominium style townhouses. The file housesales.txt provides information on the selling price (PRICE), lot cost (COST), type of home HOME ISF-single family home or T-condominium style) and region of the country (REGION) M-Midwest, S-south) for closings during one month. a) b) c) Define the dummy variables for region and home (write them down here), and create them in Analyze the association between selling price and each individual attribute (cost, home and region) using appropriate statistics and graphs. Discuss your findings. Include the relevant output. Fit an adequate regression model for sales price as a function of lot cost, region of country, and type of home. Remove the terms that are not significant. The final model should only contain variables that are significantly associated with sale price. Write down the model equation. Include the relevant output. d) Conduct a global F-test for overall model adequacy. Write down the test hypotheses and test statistic and discuss conclusions. Include the relevant output. Analyze model residuals to check if assumptions on data are satisfied. Discuss your findings. Include the relevant output. Discuss what the regression model indicates for the relationship between price and home type i.e. interpret the coefficient values). e) g) Use the regression analysis to determine whether mean sale prices are different for the two regions? Explain. Copy and paste your FULL SAS code into the word document along with your answers. h)