Calculation based on governmental and not for profit entities | prepare financial statement for governmental and not for profit entities | American Association of Collegiate Registrars and Admissions Officers

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Q3. The city council of E. Staatsboro approved the following budget for the General Fund for fiscal year 2013:

Estimated Revenues

Property taxes.. $335,000

License fees… 40,000

Fines and penalties .15,000

Total revenues .$390,000

Appropriations

Salaries. $350,000

Supplies and utilities. 30,000

Debt service. 3,000

Total appropriations.. 383,000

Budgeted Increase in Fund Balance $ 7,000

The post-closing trial balance for the fund, as of December 31, 2012, was as follows:

Debits

Credits

Cash

$ 15,000

$ 8,000

Vouchers payable

7,000

Fund balance (unassigned)

$15,000

The following transactions and events occurred during FY 2013:

1. Levied property taxes of $335,000 and mailed tax bills to property owners.

2. Borrowed $300,000 on tax anticipation notes at an interest rate of 1 percent per annum.

3. Ordered supplies expected to cost $18,000.

4. Received the supplies along with an invoice for $19,000; paid the invoice immediately.

5. Received cash ($383,000) from the following sources: property taxes ($330,000), licenses and fees ($38,000), fines and penalties ($15,000).

6. Paid cash for the following purposes: unpaid vouchers at the start of year ($8,000), salaries ($340,000), utility bills ($11,000).

7. Repaid the tax anticipation notes 6 months after the date of borrowing, with interest.

8. Processed a budgetary interchange, increasing the appropriation for supplies and utilities by $2,000 and reducing the appropriation for salaries by a like amount.

9. Will pay salaries for the last few days in December, amounting to $2,000, at the end of the first pay period in January 2014; also, received in early January 2014 a utilities invoice for $1,000 applicable to December 2013.

Use the preceding information to do the following.

a. Prepare journal entries to record the budget and the listed transactions and events.

b. Prepare a pre-closing trial balance.

c. Prepare a balance sheet; a statement of revenues, expenditures, and changes in fund balance.

d. Prepare closing journal entries.

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July 1, 2023

2.The cost of unemployment is a major issue both at the state and federal levels. What drives the cost is not only the number of unemployed but also the length of unemployment for each person. The longer a person is unemployed, the higher the cost to governments and businesses. To address the factors driving the length of unemployment for the manufacturing sector, data was collected on the number of weeks a person is unemployed due to a layoff along with a series of independent variables as shown below.

Edu:The number of years of education.

Married:An indicator of whether (Y) or not (N) a person is married.

Head:An indicator of whether (Y) or not (N) the person is the head of a household.

Tenure:The number of years on the most recent job.

Manager:An indicator of whether (Y) or not (N) the person was in a management position. Sales:An indicator of whether (Y) or not (N) the person was in a sales occupation.

Age:The current age (in years) of the individual worker

The table shown below is data for 50 displaced workers. This data set is also on Canvass (Exam folder) in Minitab format (LAYOFFS.MTW) so you don’t have to re-enter the data.

(a) Develop an estimated regression model to predict length of unemployment using all of the variables.

Regression Analysis: Weeks versus Age, Educ, ...

The regression equation is

Weeks = 22.9 + 1.51 Age - 0.613 Educ - 10.7 Married - 19.8 Head + 0.426 Tenure

- 26.7 Manager - 18.6 Sales

Predictor Coef SECoef T P

Constant 22.85 18.87 1.21 0.233

Age 1.5093 0.3040 4.96 0.000

Educ -0.6133 0.9362 -0.66 0.516

Married -10.743 6.012 -1.79 0.081

Head -19.779 5.837 -3.39 0.002

Tenure 0.4265 0.4669 0.91 0.366

Manager -26.742 8.326 -3.21 0.003

Sales -18.561 6.281 -2.96 0.005

S = 16.3497 R-Sq = 59.1% R-Sq(adj) = 52.3%

Analysis of Variance

Source DF SS MS F P

Regression 7 16250.4 2321.5 8.68 0.000

Residual Error 42 11227.1 267.3

Total 49 27477.5

Source DF Seq SS

Age 1 9161.4

Educ 1 71.6

Married 1 7.0

Head 1 2064.6

Tenure 1 666.8

Manager 1 1944.4

Sales 1 2334.6

Unusual Observations

ObsAge Weeks Fit SE Fit Residual St Resid

10 33.0 13.00 46.37 5.51 -33.37 -2.17R

24 23.0 7.00 38.47 5.81 -31.47 -2.06R

R denotes an observation with a large standardized residual.

(b) State the hypotheses one needs to test for significance of the model developed in part (a).

(d) State and interpret the R2 value.

(e) Conduct a residual analysis and comment on the results.

(f) Are all of the variables in the full model significant? If not, which ones are not significant? Explain.

(g) Is there any problem with multicollinearity? Explain. If so what should one do about it?

Correlations: Age, Educ, Married, Head, Tenure, Manager, Sales

Age Educ Married Head Tenure Manager

Educ 0.100

0.490

Married -0.209 -0.151

0.145 0.296

Head 0.027 -0.156 -0.449

0.854 0.280 0.001

Tenure 0.459 0.174 -0.057 -0.046

0.001 0.228 0.692 0.750

Manager 0.097 0.160 0.073 -0.200 -0.113

0.504 0.266 0.616 0.164 0.435

Sales 0.137 0.124 -0.148 -0.013 0.097 -0.156

0.343 0.393 0.306 0.926 0.504 0.279

Cell Contents: Pearson correlation

P-Value

(h) Develop your own model which you think is the most appropriate predictor of weeks of unemployment. Explain the results and why you think this is the best model.

Regression Analysis: Weeks versus Age, Head, Manager, Sales

The regression equation is

Weeks = - 0.07 + 1.73 Age - 15.1 Head - 28.7 Manager - 17.4 Sales

Predictor Coef SECoef T P

Constant -0.069 9.843 -0.01 0.994

Age 1.7252 0.2651 6.51 0.000

Head -15.086 5.121 -2.95 0.005

Manager -28.672 8.117 -3.53 0.001

Sales -17.421 6.236 -2.79 0.008

S = 16.5069 R-Sq = 55.4% R-Sq(adj) = 51.4%

Analysis of Variance

Source DF SS MS F P

Regression 4 15216.0 3804.0 13.96 0.000

Residual Error 45 12261.5 272.5

Total 49 27477.5

Source DF Seq SS

Age 1 9161.4

Head 1 1339.8

Manager 1 2588.1

Sales 1 2126.7

Unusual Observations

ObsAge Weeks Fit SE Fit Residual St Resid

24 23.0 7.00 39.61 5.29 -32.61 -2.09R

39 62.0 80.00 89.47 9.36 -9.47 -0.70 X

R denotes an observation with a large standardized residual.

X denotes an observation whose X value gives it large leverage.

Correlations: Age, Head, Manager, Sales

Age Head Manager

Head 0.027

0.854

Manager 0.097 -0.200

0.504 0.164

Sales 0.137 -0.013 -0.156

0.343 0.926 0.279

Cell Contents: Pearson correlation

P-Value

(i) Using your best model, what would be the estimated length of unemployment a person with the following characteristics: Age = 40; Education level = 16 years; Married; Head of household; Tenure = 18 years; Not a manager; Not in a sales occupation.

Predicted Values for New Observations

New ObsFit SE Fit 95% CI 95% PI

1 53.85 3.50 (46.81, 60.89) (19.87, 87.84)

Values of Predictors for New Observations

New ObsAge Head Manager Sales

1 40.0 1.00 0.000000 0.000000

(j) Develop and interpret a 95% confidence interval and prediction interval for the individual described in part (i) above.

(k) How might the results of your model be used to help both state and federal government deal with the length of unemployment problem?

July 1, 2023

Edu:The number of years of education.

Married:An indicator of whether (Y) or not (N) a person is married.

Head:An indicator of whether (Y) or not (N) the person is the head of a household.

Tenure:The number of years on the most recent job.

Manager:An indicator of whether (Y) or not (N) the person was in a management position. Sales:An indicator of whether (Y) or not (N) the person was in a sales occupation.

Age:The current age (in years) of the individual worker

The table shown below is data for 50 displaced workers. This data set is also on Canvass (Exam folder) in Minitab format (LAYOFFS.MTW) so you don’t have to re-enter the data.

(a) Develop an estimated regression model to predict length of unemployment using all of the variables.

Regression Analysis: Weeks versus Age, Educ, ...

The regression equation is

Weeks = 22.9 + 1.51 Age - 0.613 Educ - 10.7 Married - 19.8 Head + 0.426 Tenure

- 26.7 Manager - 18.6 Sales

Predictor Coef SECoef T P

Constant 22.85 18.87 1.21 0.233

Age 1.5093 0.3040 4.96 0.000

Educ -0.6133 0.9362 -0.66 0.516

Married -10.743 6.012 -1.79 0.081

Head -19.779 5.837 -3.39 0.002

Tenure 0.4265 0.4669 0.91 0.366

Manager -26.742 8.326 -3.21 0.003

Sales -18.561 6.281 -2.96 0.005

S = 16.3497 R-Sq = 59.1% R-Sq(adj) = 52.3%

Analysis of Variance

Source DF SS MS F P

Regression 7 16250.4 2321.5 8.68 0.000

Residual Error 42 11227.1 267.3

Total 49 27477.5

Source DF Seq SS

Age 1 9161.4

Educ 1 71.6

Married 1 7.0

Head 1 2064.6

Tenure 1 666.8

Manager 1 1944.4

Sales 1 2334.6

Unusual Observations

ObsAge Weeks Fit SE Fit Residual St Resid

10 33.0 13.00 46.37 5.51 -33.37 -2.17R

24 23.0 7.00 38.47 5.81 -31.47 -2.06R

R denotes an observation with a large standardized residual.

(b) State the hypotheses one needs to test for significance of the model developed in part (a).

(d) State and interpret the R2 value.

(e) Conduct a residual analysis and comment on the results.

(f) Are all of the variables in the full model significant? If not, which ones are not significant? Explain.

(g) Is there any problem with multicollinearity? Explain. If so what should one do about it?

Correlations: Age, Educ, Married, Head, Tenure, Manager, Sales

Age Educ Married Head Tenure Manager

Educ 0.100

0.490

Married -0.209 -0.151

0.145 0.296

Head 0.027 -0.156 -0.449

0.854 0.280 0.001

Tenure 0.459 0.174 -0.057 -0.046

0.001 0.228 0.692 0.750

Manager 0.097 0.160 0.073 -0.200 -0.113

0.504 0.266 0.616 0.164 0.435

Sales 0.137 0.124 -0.148 -0.013 0.097 -0.156

0.343 0.393 0.306 0.926 0.504 0.279

Cell Contents: Pearson correlation

P-Value

(h) Develop your own model which you think is the most appropriate predictor of weeks of unemployment. Explain the results and why you think this is the best model.

Regression Analysis: Weeks versus Age, Head, Manager, Sales

The regression equation is

Weeks = - 0.07 + 1.73 Age - 15.1 Head - 28.7 Manager - 17.4 Sales

Predictor Coef SECoef T P

Constant -0.069 9.843 -0.01 0.994

Age 1.7252 0.2651 6.51 0.000

Head -15.086 5.121 -2.95 0.005

Manager -28.672 8.117 -3.53 0.001

Sales -17.421 6.236 -2.79 0.008

S = 16.5069 R-Sq = 55.4% R-Sq(adj) = 51.4%

Analysis of Variance

Source DF SS MS F P

Regression 4 15216.0 3804.0 13.96 0.000

Residual Error 45 12261.5 272.5

Total 49 27477.5

Source DF Seq SS

Age 1 9161.4

Head 1 1339.8

Manager 1 2588.1

Sales 1 2126.7

Unusual Observations

ObsAge Weeks Fit SE Fit Residual St Resid

24 23.0 7.00 39.61 5.29 -32.61 -2.09R

39 62.0 80.00 89.47 9.36 -9.47 -0.70 X

R denotes an observation with a large standardized residual.

X denotes an observation whose X value gives it large leverage.

Correlations: Age, Head, Manager, Sales

Age Head Manager

Head 0.027

0.854

Manager 0.097 -0.200

0.504 0.164

Sales 0.137 -0.013 -0.156

0.343 0.926 0.279

Cell Contents: Pearson correlation

P-Value

Predicted Values for New Observations

New ObsFit SE Fit 95% CI 95% PI

1 53.85 3.50 (46.81, 60.89) (19.87, 87.84)

Values of Predictors for New Observations

New ObsAge Head Manager Sales

1 40.0 1.00 0.000000 0.000000

(j) Develop and interpret a 95% confidence interval and prediction interval for the individual described in part (i) above.

(k) How might the results of your model be used to help both state and federal government deal with the length of unemployment problem?

July 1, 2023

Edu:The number of years of education.

Married:An indicator of whether (Y) or not (N) a person is married.

Head:An indicator of whether (Y) or not (N) the person is the head of a household.

Tenure:The number of years on the most recent job.

Manager:An indicator of whether (Y) or not (N) the person was in a management position. Sales:An indicator of whether (Y) or not (N) the person was in a sales occupation.

Age:The current age (in years) of the individual worker

The table shown below is data for 50 displaced workers. This data set is also on Canvass (Exam folder) in Minitab format (LAYOFFS.MTW) so you don’t have to re-enter the data.

(a) Develop an estimated regression model to predict length of unemployment using all of the variables.

Regression Analysis: Weeks versus Age, Educ, ...

The regression equation is

Weeks = 22.9 + 1.51 Age - 0.613 Educ - 10.7 Married - 19.8 Head + 0.426 Tenure

- 26.7 Manager - 18.6 Sales

Predictor Coef SECoef T P

Constant 22.85 18.87 1.21 0.233

Age 1.5093 0.3040 4.96 0.000

Educ -0.6133 0.9362 -0.66 0.516

Married -10.743 6.012 -1.79 0.081

Head -19.779 5.837 -3.39 0.002

Tenure 0.4265 0.4669 0.91 0.366

Manager -26.742 8.326 -3.21 0.003

Sales -18.561 6.281 -2.96 0.005

S = 16.3497 R-Sq = 59.1% R-Sq(adj) = 52.3%

Analysis of Variance

Source DF SS MS F P

Regression 7 16250.4 2321.5 8.68 0.000

Residual Error 42 11227.1 267.3

Total 49 27477.5

Source DF Seq SS

Age 1 9161.4

Educ 1 71.6

Married 1 7.0

Head 1 2064.6

Tenure 1 666.8

Manager 1 1944.4

Sales 1 2334.6

Unusual Observations

ObsAge Weeks Fit SE Fit Residual St Resid

10 33.0 13.00 46.37 5.51 -33.37 -2.17R

24 23.0 7.00 38.47 5.81 -31.47 -2.06R

R denotes an observation with a large standardized residual.

(b) State the hypotheses one needs to test for significance of the model developed in part (a).

(d) State and interpret the R2 value.

(e) Conduct a residual analysis and comment on the results.

(f) Are all of the variables in the full model significant? If not, which ones are not significant? Explain.

(g) Is there any problem with multicollinearity? Explain. If so what should one do about it?

Correlations: Age, Educ, Married, Head, Tenure, Manager, Sales

Age Educ Married Head Tenure Manager

Educ 0.100

0.490

Married -0.209 -0.151

0.145 0.296

Head 0.027 -0.156 -0.449

0.854 0.280 0.001

Tenure 0.459 0.174 -0.057 -0.046

0.001 0.228 0.692 0.750

Manager 0.097 0.160 0.073 -0.200 -0.113

0.504 0.266 0.616 0.164 0.435

Sales 0.137 0.124 -0.148 -0.013 0.097 -0.156

0.343 0.393 0.306 0.926 0.504 0.279

Cell Contents: Pearson correlation

P-Value

(h) Develop your own model which you think is the most appropriate predictor of weeks of unemployment. Explain the results and why you think this is the best model.

Regression Analysis: Weeks versus Age, Head, Manager, Sales

The regression equation is

Weeks = - 0.07 + 1.73 Age - 15.1 Head - 28.7 Manager - 17.4 Sales

Predictor Coef SECoef T P

Constant -0.069 9.843 -0.01 0.994

Age 1.7252 0.2651 6.51 0.000

Head -15.086 5.121 -2.95 0.005

Manager -28.672 8.117 -3.53 0.001

Sales -17.421 6.236 -2.79 0.008

S = 16.5069 R-Sq = 55.4% R-Sq(adj) = 51.4%

Analysis of Variance

Source DF SS MS F P

Regression 4 15216.0 3804.0 13.96 0.000

Residual Error 45 12261.5 272.5

Total 49 27477.5

Source DF Seq SS

Age 1 9161.4

Head 1 1339.8

Manager 1 2588.1

Sales 1 2126.7

Unusual Observations

ObsAge Weeks Fit SE Fit Residual St Resid

24 23.0 7.00 39.61 5.29 -32.61 -2.09R

39 62.0 80.00 89.47 9.36 -9.47 -0.70 X

R denotes an observation with a large standardized residual.

X denotes an observation whose X value gives it large leverage.

Correlations: Age, Head, Manager, Sales

Age Head Manager

Head 0.027

0.854

Manager 0.097 -0.200

0.504 0.164

Sales 0.137 -0.013 -0.156

0.343 0.926 0.279

Cell Contents: Pearson correlation

P-Value

Predicted Values for New Observations

New ObsFit SE Fit 95% CI 95% PI

1 53.85 3.50 (46.81, 60.89) (19.87, 87.84)

Values of Predictors for New Observations

New ObsAge Head Manager Sales

1 40.0 1.00 0.000000 0.000000

(j) Develop and interpret a 95% confidence interval and prediction interval for the individual described in part (i) above.

(k) How might the results of your model be used to help both state and federal government deal with the length of unemployment problem?

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