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[>Management homework help](https://www.sweetstudy.com/fields/business-finance-management) – 2 years ago **files (3)** – ## PatriotBank.xlsx – ## Chapter9Lab-MGMT414-S24.docx.pdf – ## BigBox.xlsx **## PatriotBank.xlsx** ## Staffing Analysis | | | | — | — | | Interest Rate | Applications | | 4.0%…
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[>Management homework help](https://www.sweetstudy.com/fields/business-finance-management)
– 2 years ago
**files (3)**
– ## PatriotBank.xlsx
– ## Chapter9Lab-MGMT414-S24.docx.pdf
– ## BigBox.xlsx
**## PatriotBank.xlsx**
## Staffing Analysis
| | |
| — | — |
| Interest Rate | Applications |
| 4.0% | 3098 |
| 4.3% | 2785 |
| 4.3% | 2515 |
| 4.7% | 2180 |
| 4.7% | 1873 |
| 5.2% | 1794 |
| 5.6% | 1450 |
| 5.7% | 1177 |
| 6.1% | 822 |
| 6.2% | 886 |
| 6.2% | 740 |
| 6.8% | 740 |
| 7.1% | 722 |
| 7.6% | 540 |
| 8.3% | 375 |
| 8.9% | 425 |
| 9.4% | 360 |
| 9.5% | 389 |
| 9.7% | 339 |
| 9.8% | 336 |
**## Chapter9Lab-MGMT414-S24.docx.pdf**
MGMT 414: Management Analytics
Lab Assignment: Chapter 9
Please see the corresponding datasets in blackboard and complete your
analysis on the first two questions. Two separate excel files are expected for
this assignment. All short answer questions should be completed in a Textbox.
Each student will complete a data analysis independently and turn in their
own file. Add your name to the file.
1. When interest rates decline, Patriot Bank has found they get inundated with
requests to refinance home mortgages. To better plan its staffing needs in the
mortgage processing area of its operations, Patriot wants to develop a
regression model to help predict the total number of mortgage applications
(Y) each month as a function of the prime interest rate (X1). The bank
collected the data shown in the file PatriotBank.xlsx representing the average
prime interest rate and total number of mortgage applications in 20 different
months.
1. Prepare a scatter plot of these data. Does there appear to be a linear
relationship between these variables.
2. Obtain a simple linear regression model by producing output tables.
3. Interpret the R^2 for the model you obtained.
4. What is the number of mortgage applications Patriot could expect to
receive in a month where the interest rate is 6%.
2. A recruiter for Big Box stores has collected the data in the file BigBox.xlsx
summarizing the amount of money the company spent on print, web, and TV
advertising in California over the past 22 months and the resulting number of
applications received from job applicants during the same months. The
recruiter would like to build a regression model to predict the number of
applications the company should expect based on a given advertising mix.
1. Prepare scatter plots showing the relationship between the number of
applications received and each of the independent variables. What sort
of relationship does each plot suggest?
2. If the recruiter wanted to build a regression model using only one
independent variable to predict the number of applications received,
what variable should be used?
3. What set of independent variables results in the highest value for the
adjusted-R 2 statistic?
4. Suppose the recruiter chooses to use the regression function with all
independent variables X1, X2, and X3. What is the estimated regression
function?
**## BigBox.xlsx**
## Data
| | | | |
| — | — | — | — |
| $ Print | $ Web | $ TV | Applications |
| 4,800 | 990 | 3,320 | 576 |
| 4,430 | 1,850 | 5,340 | 769 |
| 4,260 | 4,920 | 6,580 | 1,193 |
| 4,260 | 4,080 | 9,360 | 1,239 |
| 4,790 | 1,430 | 2,660 | 531 |
| 1,800 | 2,990 | 4,720 | 665 |
| 3,100 | 4,710 | 1,680 | 763 |
| 1,090 | 1,780 | 4,360 | 508 |
| 3,630 | 1,350 | 9,560 | 871 |
| 2,200 | 4,270 | 9,540 | 1,163 |
| 2,830 | 4,700 | 9,180 | 1,188 |
| 670 | 2,160 | 2,780 | 420 |
| 3,600 | 1,000 | 4,580 | 539 |
| 100 | 3,930 | 6,880 | 827 |
| 1,410 | 3,320 | 9,740 | 972 |
| 370 | 2,000 | 9,440 | 759 |
| 2,640 | 2,070 | 7,740 | 801 |
| 4,690 | 370 | 7,940 | 740 |
| 760 | 890 | 1,700 | 205 |
| 870 | 1,360 | 900 | 214 |
| 2,730 | 510 | 1,180 | 264 |
| 1,750 | 2,790 | 1,340 | 459 |
**## BigBox.xlsx**
## Data
| | | | |
| — | — | — | — |
| $ Print | $ Web | $ TV | Applications |
| 4,800 | 990 | 3,320 | 576 |
| 4,430 | 1,850 | 5,340 | 769 |
| 4,260 | 4,920 | 6,580 | 1,193 |
| 4,260 | 4,080 | 9,360 | 1,239 |
| 4,790 | 1,430 | 2,660 | 531 |
| 1,800 | 2,990 | 4,720 | 665 |
| 3,100 | 4,710 | 1,680 | 763 |
| 1,090 | 1,780 | 4,360 | 508 |
| 3,630 | 1,350 | 9,560 | 871 |
| 2,200 | 4,270 | 9,540 | 1,163 |
| 2,830 | 4,700 | 9,180 | 1,188 |
| 670 | 2,160 | 2,780 | 420 |
| 3,600 | 1,000 | 4,580 | 539 |
| 100 | 3,930 | 6,880 | 827 |
| 1,410 | 3,320 | 9,740 | 972 |
| 370 | 2,000 | 9,440 | 759 |
| 2,640 | 2,070 | 7,740 | 801 |
| 4,690 | 370 | 7,940 | 740 |
| 760 | 890 | 1,700 | 205 |
| 870 | 1,360 | 900 | 214 |
| 2,730 | 510 | 1,180 | 264 |
| 1,750 | 2,790 | 1,340 | 459 |
**## PatriotBank.xlsx**
## Staffing Analysis
| | |
| — | — |
| Interest Rate | Applications |
| 4.0% | 3098 |
| 4.3% | 2785 |
| 4.3% | 2515 |
| 4.7% | 2180 |
| 4.7% | 1873 |
| 5.2% | 1794 |
| 5.6% | 1450 |
| 5.7% | 1177 |
| 6.1% | 822 |
| 6.2% | 886 |
| 6.2% | 740 |
| 6.8% | 740 |
| 7.1% | 722 |
| 7.6% | 540 |
| 8.3% | 375 |
| 8.9% | 425 |
| 9.4% | 360 |
| 9.5% | 389 |
| 9.7% | 339 |
| 9.8% | 336 |
**## Chapter9Lab-MGMT414-S24.docx.pdf**
MGMT 414: Management Analytics
Lab Assignment: Chapter 9
Please see the corresponding datasets in blackboard and complete your
analysis on the first two questions. Two separate excel files are expected for
this assignment. All short answer questions should be completed in a Textbox.
Each student will complete a data analysis independently and turn in their
own file. Add your name to the file.
1. When interest rates decline, Patriot Bank has found they get inundated with
requests to refinance home mortgages. To better plan its staffing needs in the
mortgage processing area of its operations, Patriot wants to develop a
regression model to help predict the total number of mortgage applications
(Y) each month as a function of the prime interest rate (X1). The bank
collected the data shown in the file PatriotBank.xlsx representing the average
prime interest rate and total number of mortgage applications in 20 different
months.
1. Prepare a scatter plot of these data. Does there appear to be a linear
relationship between these variables.
2. Obtain a simple linear regression model by producing output tables.
3. Interpret the R^2 for the model you obtained.
4. What is the number of mortgage applications Patriot could expect to
receive in a month where the interest rate is 6%.
2. A recruiter for Big Box stores has collected the data in the file BigBox.xlsx
summarizing the amount of money the company spent on print, web, and TV
advertising in California over the past 22 months and the resulting number of
applications received from job applicants during the same months. The
recruiter would like to build a regression model to predict the number of
applications the company should expect based on a given advertising mix.
1. Prepare scatter plots showing the relationship between the number of
applications received and each of the independent variables. What sort
of relationship does each plot suggest?
2. If the recruiter wanted to build a regression model using only one
independent variable to predict the number of applications received,
what variable should be used?
3. What set of independent variables results in the highest value for the
adjusted-R 2 statistic?
4. Suppose the recruiter chooses to use the regression function with all
independent variables X1, X2, and X3. What is the estimated regression
function?
**## BigBox.xlsx**
## Data
| | | | |
| — | — | — | — |
| $ Print | $ Web | $ TV | Applications |
| 4,800 | 990 | 3,320 | 576 |
| 4,430 | 1,850 | 5,340 | 769 |
| 4,260 | 4,920 | 6,580 | 1,193 |
| 4,260 | 4,080 | 9,360 | 1,239 |
| 4,790 | 1,430 | 2,660 | 531 |
| 1,800 | 2,990 | 4,720 | 665 |
| 3,100 | 4,710 | 1,680 | 763 |
| 1,090 | 1,780 | 4,360 | 508 |
| 3,630 | 1,350 | 9,560 | 871 |
| 2,200 | 4,270 | 9,540 | 1,163 |
| 2,830 | 4,700 | 9,180 | 1,188 |
| 670 | 2,160 | 2,780 | 420 |
| 3,600 | 1,000 | 4,580 | 539 |
| 100 | 3,930 | 6,880 | 827 |
| 1,410 | 3,320 | 9,740 | 972 |
| 370 | 2,000 | 9,440 | 759 |
| 2,640 | 2,070 | 7,740 | 801 |
| 4,690 | 370 | 7,940 | 740 |
| 760 | 890 | 1,700 | 205 |
| 870 | 1,360 | 900 | 214 |
| 2,730 | 510 | 1,180 | 264 |
| 1,750 | 2,790 | 1,340 | 459 |
**## PatriotBank.xlsx**
## Staffing Analysis
| | |
| — | — |
| Interest Rate | Applications |
| 4.0% | 3098 |
| 4.3% | 2785 |
| 4.3% | 2515 |
| 4.7% | 2180 |
| 4.7% | 1873 |
| 5.2% | 1794 |
| 5.6% | 1450 |
| 5.7% | 1177 |
| 6.1% | 822 |
| 6.2% | 886 |
| 6.2% | 740 |
| 6.8% | 740 |
| 7.1% | 722 |
| 7.6% | 540 |
| 8.3% | 375 |
| 8.9% | 425 |
| 9.4% | 360 |
| 9.5% | 389 |
| 9.7% | 339 |
| 9.8% | 336 |
**## Chapter9Lab-MGMT414-S24.docx.pdf**
MGMT 414: Management Analytics
Lab Assignment: Chapter 9
Please see the corresponding datasets in blackboard and complete your
analysis on the first two questions. Two separate excel files are expected for
this assignment. All short answer questions should be completed in a Textbox.
Each student will complete a data analysis independently and turn in their
own file. Add your name to the file.
1. When interest rates decline, Patriot Bank has found they get inundated with
requests to refinance home mortgages. To better plan its staffing needs in the
mortgage processing area of its operations, Patriot wants to develop a
regression model to help predict the total number of mortgage applications
(Y) each month as a function of the prime interest rate (X1). The bank
collected the data shown in the file PatriotBank.xlsx representing the average
prime interest rate and total number of mortgage applications in 20 different
months.
1. Prepare a scatter plot of these data. Does there appear to be a linear
relationship between these variables.
2. Obtain a simple linear regression model by producing output tables.
3. Interpret the R^2 for the model you obtained.
4. What is the number of mortgage applications Patriot could expect to
receive in a month where the interest rate is 6%.
2. A recruiter for Big Box stores has collected the data in the file BigBox.xlsx
summarizing the amount of money the company spent on print, web, and TV
advertising in California over the past 22 months and the resulting number of
applications received from job applicants during the same months. The
recruiter would like to build a regression model to predict the number of
applications the company should expect based on a given advertising mix.
1. Prepare scatter plots showing the relationship between the number of
applications received and each of the independent variables. What sort
of relationship does each plot suggest?
2. If the recruiter wanted to build a regression model using only one
independent variable to predict the number of applications received,
what variable should be used?
3. What set of independent variables results in the highest value for the
adjusted-R 2 statistic?
4. Suppose the recruiter chooses to use the regression function with all
independent variables X1, X2, and X3. What is the estimated regression
function?
**## BigBox.xlsx**
## Data
| | | | |
| — | — | — | — |
| $ Print | $ Web | $ TV | Applications |
| 4,800 | 990 | 3,320 | 576 |
| 4,430 | 1,850 | 5,340 | 769 |
| 4,260 | 4,920 | 6,580 | 1,193 |
| 4,260 | 4,080 | 9,360 | 1,239 |
| 4,790 | 1,430 | 2,660 | 531 |
| 1,800 | 2,990 | 4,720 | 665 |
| 3,100 | 4,710 | 1,680 | 763 |
| 1,090 | 1,780 | 4,360 | 508 |
| 3,630 | 1,350 | 9,560 | 871 |
| 2,200 | 4,270 | 9,540 | 1,163 |
| 2,830 | 4,700 | 9,180 | 1,188 |
| 670 | 2,160 | 2,780 | 420 |
| 3,600 | 1,000 | 4,580 | 539 |
| 100 | 3,930 | 6,880 | 827 |
| 1,410 | 3,320 | 9,740 | 972 |
| 370 | 2,000 | 9,440 | 759 |
| 2,640 | 2,070 | 7,740 | 801 |
| 4,690 | 370 | 7,940 | 740 |
| 760 | 890 | 1,700 | 205 |
| 870 | 1,360 | 900 | 214 |
| 2,730 | 510 | 1,180 | 264 |
| 1,750 | 2,790 | 1,340 | 459 |
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