Differentiates cultural and religious customs and practices

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INTRODUCTION

Christianity and Islam are the two largest religions in the world and have influenced the art, politics, and culture of societies around the globe for the last 1,500 years. Over the centuries, these two Abrahamic religions, which share foundational beliefs, have evolved significantly as new adherents incorporated local customs into their religious practices and schisms occurred over conflicting theology and leadership disputes.

In this task, you will examine the spread of Islam and Christianity throughout Africa and Eurasia from each religion’s place of origin. You will also examine how each religion spread as well as how each religion changed as it was adopted by people from different cultural backgrounds.

REQUIREMENTS

Your submission must be your original work. No more than a combined total of 30% of the submission and no more than a 10% match to any one individual source can be directly quoted or closely paraphrased from sources, even if cited correctly. The similarity report that is provided when you submit your task can be used as a guide.

You must use the rubric to direct the creation of your submission because it provides detailed criteria that will be used to evaluate your work. Each requirement below may be evaluated by more than one rubric aspect. The rubric aspect titles may contain hyperlinks to relevant portions of the course.

Tasks may not be submitted as cloud links, such as links to Google Docs, Google Slides, OneDrive, etc., unless specified in the task requirements. All other submissions must be file types that are uploaded and submitted as attachments (e.g., .docx, .pdf, .ppt).

A. Analyze, including specific examples, the similarities and differences in the diffusion of Christianity and Islam (suggested length of 1–2 pages) from their founding until 1500 C.E. by doing the following:

1. Describe one similarity in how Christianity and Islam spread from their places of origin.

2. Describe one difference in how Christianity and Islam spread from their places of origin.

3. Explain why the beliefs and practices of Christianity and Islam changed as they spread.

B. Acknowledge sources, using in-text citations and references, for content that is quoted, paraphrased, or summarized.

C. Demonstrate professional communication in the content and presentation of your submission.

RUBRICA1. SIMILARITY IN SPREAD:

COMPETENT

The submission accurately describes 1 similarity in the spread of the given religions from their places of origin, and the description includes specific, detailed examples.

A2. DIFFERENCE IN SPREAD:

COMPETENT

The submission accurately describes 1 difference in the spread of the given religions from their places of origin, and the description includes specific, detailed examples.

A3. CHANGE IN BELIEFS AND PRACTICES:

COMPETENT

The submission accurately explains why the beliefs and practices of the given religions changed as they spread, and the explanation includes specific, detailed examples.

B. SOURCES:

COMPETENT

The submission includes in-text citations for sources that are properly quoted, paraphrased, or summarized and a reference list that accurately identifies the author, date, title, and source location as available.

C. PROFESSIONAL COMMUNICATION:

COMPETENT

Content reflects attention to detail, is organized, and focuses on the main ideas as prescribed in the task or chosen by the candidate. Terminology is pertinent, is used correctly, and effectively conveys the intended meaning. Mechanics, usage, and grammar promote accurate interpretation and understanding.

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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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