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I have about 40 more...

I have about 40 more minutes to turn in my statistic assignment and need help. Is there someone available to assist me? All questions are multiple chose except for one.

#1 Suppose the following data represent the results of this survey.

Assume that relationship with supervisor is rated on a scale from 0 to 50 (0 represents poor relationship and 50 represents an excellent relationship), overall quality of the work environment is rated on a scale from 0 to 100 (0 represents poor work environment and 100 rep resents an excellent work environment), and opportunities for advancement

is rated on a scale from 0 to 50 (0 represents no opportunities and 50 represents excellent opportunities).

Answer the following questions:

Job Relationship with Overall quality of Total hours Opportunities

Satisfaction supervisor work worked per week for advancement

55 27 65 50 42

20 12 13 60 28

85 40 79 45 7

65 35 53 65 48

45 29 43 40 32

70 42 62 50 41

35 22 18 75 18

60 34 75 40 32

95 50 84 45 48

65 33 68 60 11

85 40 72 55 33

10 5 10 50 21

75 37 64 45 42

80 42 82 40 46

50 31 46 60 48

90 47 95 55 30

75 36 82 70 39

45 20 42 40 22

65 32 73 55 12

1. According to the following graphic, X and Y have _________.

(Points : 4)

strong negative correlation

virtually no correlation

strong positive correlation

moderate negative correlation

weak negative correlation

2. A cost accountant is developing a regression model to predict the total cost of producing a batch of printed circuit boards as a function of batch size (the number of boards produced in one lot or batch). The dependent variable is ______. (Points : 4)

batch size

unit variable cost

fixed cost

total cost

total variable cost

3. A cost accountant is developing a regression model to predict the total cost of producing a batch of printed circuit boards as a linear function of batch size (the number of boards produced in one lot or batch). The intercept of this model is the ______. (Points : 4)

batch size

unit variable cost

fixed cost

total cost

total variable cost

4. If x and y in a regression model are totally unrelated, _______. (Points : 4)

the correlation coefficient would be -1

the coefficient of determination would be 0

the coefficient of determination would be 1

the SSE would be 0

the MSE would be 0s

5. A manager wishes to predict the annual cost (y) of an automobile based on the number of miles (x) driven. The following model was developed:

y= 1,550 + 0.36x. If a car is driven 15,000 miles, the predicted cost is ____________. (Points : 4)

2090

3850

7400

6950

5400

6. A cost accountant is developing a regression model to predict the total cost of producing a batch of printed circuit boards as a linear function of batch size (the number of boards produced in one lot or batch), production plant (Kingsland, and Yorktown), and production shift (day, and evening). In this model, "shift" is ______. (Points : 4)

a response variable

an independent variable

a quantitative variable

a dependent variable

a constant

7. A multiple regression analysis produced the following tables.

Predictor

Coefficients

Standard Error

t Statistic

p-value

Intercept

(NNN) NNN-NNNN

(NNN) NNN-NNNN

3.990108

0.000947

x1

-3.33833

2.333548

-1.43058

0.170675

x2

1.780075

0.335605

5.30407

5.83E-05

Source

df

SS

MS

F

p-value

Regression

2

121783

60891.48

14.76117

0.000286

Residual

15

61876.68

4125.112

Total

17

183659.6

The regression equation for this analysis is ____________. (Points : 4)

y =(NNN) NNN-NNNN+ 3.33833 x1 + 1.780075 x2

y =(NNN) NNN-NNNN- 1.43058 x1 + 5.30407 x2

y =(NNN) NNN-NNNN- 3.33833 x1 - 1.780075 x2

y =(NNN) NNN-NNNN+ 2.333548 x1 + 0.335605 x2

y =(NNN) NNN-NNNN- 3.33833 x1 + 1.780075 x2

8. A multiple regression analysis produced the following tables.

Predictor

Coefficients

Standard Error

t Statistic

p-value

Intercept

(NNN) NNN-NNNN

(NNN) NNN-NNNN

2.236241

0.042132

x1

11.87375

5.32047

2.231711

0.042493

x2

1.908183

0.662742

2.879226

0.01213

Source

df

SS

MS

F

p-value

Regression

2

203693.3

101846.7

6.745406

0.010884

Residual

12

181184.1

15098.67

Total

14

384877.4

These results indicate that ____________. (Points : 4)

none of the predictor variables are significant at the 5% level

each predictor variable is significant at the 5% level

x1 is the only predictor variable significant at the 5% level

x2 is the only predictor variable significant at the 5% level

the intercept is not significant at the 5% level

9. A real estate appraiser is developing a regression model to predict the market value of single family res

#1 Suppose the following data represent the results of this survey.

Assume that relationship with supervisor is rated on a scale from 0 to 50 (0 represents poor relationship and 50 represents an excellent relationship), overall quality of the work environment is rated on a scale from 0 to 100 (0 represents poor work environment and 100 rep resents an excellent work environment), and opportunities for advancement

is rated on a scale from 0 to 50 (0 represents no opportunities and 50 represents excellent opportunities).

Answer the following questions:

Job Relationship with Overall quality of Total hours Opportunities

Satisfaction supervisor work worked per week for advancement

55 27 65 50 42

20 12 13 60 28

85 40 79 45 7

65 35 53 65 48

45 29 43 40 32

70 42 62 50 41

35 22 18 75 18

60 34 75 40 32

95 50 84 45 48

65 33 68 60 11

85 40 72 55 33

10 5 10 50 21

75 37 64 45 42

80 42 82 40 46

50 31 46 60 48

90 47 95 55 30

75 36 82 70 39

45 20 42 40 22

65 32 73 55 12

1. According to the following graphic, X and Y have _________.

(Points : 4)

strong negative correlation

virtually no correlation

strong positive correlation

moderate negative correlation

weak negative correlation

2. A cost accountant is developing a regression model to predict the total cost of producing a batch of printed circuit boards as a function of batch size (the number of boards produced in one lot or batch). The dependent variable is ______. (Points : 4)

batch size

unit variable cost

fixed cost

total cost

total variable cost

3. A cost accountant is developing a regression model to predict the total cost of producing a batch of printed circuit boards as a linear function of batch size (the number of boards produced in one lot or batch). The intercept of this model is the ______. (Points : 4)

batch size

unit variable cost

fixed cost

total cost

total variable cost

4. If x and y in a regression model are totally unrelated, _______. (Points : 4)

the correlation coefficient would be -1

the coefficient of determination would be 0

the coefficient of determination would be 1

the SSE would be 0

the MSE would be 0s

5. A manager wishes to predict the annual cost (y) of an automobile based on the number of miles (x) driven. The following model was developed:

y= 1,550 + 0.36x. If a car is driven 15,000 miles, the predicted cost is ____________. (Points : 4)

2090

3850

7400

6950

5400

6. A cost accountant is developing a regression model to predict the total cost of producing a batch of printed circuit boards as a linear function of batch size (the number of boards produced in one lot or batch), production plant (Kingsland, and Yorktown), and production shift (day, and evening). In this model, "shift" is ______. (Points : 4)

a response variable

an independent variable

a quantitative variable

a dependent variable

a constant

7. A multiple regression analysis produced the following tables.

Predictor

Coefficients

Standard Error

t Statistic

p-value

Intercept

(NNN) NNN-NNNN

(NNN) NNN-NNNN

3.990108

0.000947

x1

-3.33833

2.333548

-1.43058

0.170675

x2

1.780075

0.335605

5.30407

5.83E-05

Source

df

SS

MS

F

p-value

Regression

2

121783

60891.48

14.76117

0.000286

Residual

15

61876.68

4125.112

Total

17

183659.6

The regression equation for this analysis is ____________. (Points : 4)

y =(NNN) NNN-NNNN+ 3.33833 x1 + 1.780075 x2

y =(NNN) NNN-NNNN- 1.43058 x1 + 5.30407 x2

y =(NNN) NNN-NNNN- 3.33833 x1 - 1.780075 x2

y =(NNN) NNN-NNNN+ 2.333548 x1 + 0.335605 x2

y =(NNN) NNN-NNNN- 3.33833 x1 + 1.780075 x2

8. A multiple regression analysis produced the following tables.

Predictor

Coefficients

Standard Error

t Statistic

p-value

Intercept

(NNN) NNN-NNNN

(NNN) NNN-NNNN

2.236241

0.042132

x1

11.87375

5.32047

2.231711

0.042493

x2

1.908183

0.662742

2.879226

0.01213

Source

df

SS

MS

F

p-value

Regression

2

203693.3

101846.7

6.745406

0.010884

Residual

12

181184.1

15098.67

Total

14

384877.4

These results indicate that ____________. (Points : 4)

none of the predictor variables are significant at the 5% level

each predictor variable is significant at the 5% level

x1 is the only predictor variable significant at the 5% level

x2 is the only predictor variable significant at the 5% level

the intercept is not significant at the 5% level

9. A real estate appraiser is developing a regression model to predict the market value of single family res

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