Gb513 unit 4 quiz latest

Question

Question 1 2 / 2 points

According to the following graphic, X and Y have:

1) strong negative correlation

2) virtually no correlation

3) strong positive correlation

4) moderate negative correlation

5) weak negative correlation

Question 2 2 / 2 points

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 independent variable is:

1) batch size

2) unit variable cost

3) fixed cost

4) total cost

5) total variable cost

Question 3 2 / 2 points

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:

1) batch size

2) unit variable cost

3) fixed cost

4) total cost

5) total variable cost

Question 4 2 / 2 points

If x and y in a regression model are totally unrelated:

1) the correlation coefficient would be -1

2) the coefficient of determination would be 0

3) the coefficient of determination would be 1

4) the SSE would be 0

5) the MSE would be 0s

Question 5 2 / 2 points

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 10,000 miles, the predicted cost is:

1) 2090

2) 3850

3) 7400

4) 6950

5) 5150

Question 6 2 / 2 points

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:

1) a response variable

2) an independent variable

3) a quantitative variable

4) a dependent variable

5) a constant

Question 7 2 / 2 points

A multiple regression analysis produced the following tables:

Predictor

Coefficients

Standard Error

tStatistic

p-value

Intercept

616.6849

154.5534

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:

1) y = 616.6849 + 3.33833 x1 + 1.780075 x2

2) y = 154.5535 – 1.43058 x1 + 5.30407 x2

3) y = 616.6849 – 3.33833 x1 – 1.780075 x2

4) y = 154.5535 + 2.333548 x1 + 0.335605 x2

5) y = 616.6849 – 3.33833 x1 + 1.780075 x2

Question 8 2 / 2 points

A multiple regression analysis produced the following tables:

Predictor

Coefficients

Standard Error

tStatistic

p-value

Intercept

752.0833

336.3158

2.236241

0.042132

x1

11.87375

5.32047

2.031711

0.082493

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:

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

2) each predictor variable is significant at the 5% level

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

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

5) the intercept is not significant at the 5% level

Question 9 2 / 2 points

A real estate appraiser is developing a regression model to predict the market value of single family residential houses as a function of heated area, number of bedrooms, number of bathrooms, age of the house, and central heating (yes, no). The response variable in this model is:

1) heated area

2) number of bedrooms

3) market value

4) central heating

5) residential houses

Question 10 2 / 2 points

In regression analysis, outliers may be identified by examining the:

1) coefficient of determination

2) coefficient of correlation

3) p-values for the partial coefficients

4) residuals

5) R-squared value

 

 

 

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