(Solved) : ~ Sas Language ~ Bankingtxt Age Education Income Balance 359 148 91033 38517 377 138 86748 Q34618862 . . .

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~ SAS Language ~

A university career center collects information on the job status and starting salary of graduating seniors. Data recently co

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 γ 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) Show transcribed image text 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 γ 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)

Expert Answer


Answer to ~ 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 3…

Description

~ SAS Language ~

A university career center collects information on the job status and starting salary of graduating seniors. Data recently co

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
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