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QUESTION

# Kaplan University, Davenport IA GB 513 Quiz

Question1. According to the following graphic, X and Y have:

(Points : 2)

strong negative correlation

virtually no correlation

strong positive correlation

moderate negative correlation

weak negative correlation

Question 2.2. 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 independent variable is: (Points : 2)

batch size

unit variable cost

fixed cost

total cost

total variable cost

Question 3.3. 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 : 2)

batch size

unit variable cost

fixed cost

total cost

total variable cost

Question 4.4. If x and y in a regression model are totally unrelated: (Points : 2)

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

Question 5.5. 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 10,000 miles, the predicted cost is: (Points : 2)

2090

3850

7400

6950

5150

Question 6.6. 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 : 2)

a response variable

an independent variable

a quantitative variable

a dependent variable

a constant

Question 7.7. A multiple regression analysis produced the following tables:

Predictor Coefficients Standard Error t Statistic 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: (Points : 2)

y = 616.6849 + 3.33833 x1 + 1.780075 x2

y = 154.5535 - 1.43058 x1 + 5.30407 x2

y = 616.6849 - 3.33833 x1 - 1.780075 x2

y = 154.5535 + 2.333548 x1 + 0.335605 x2

y = 616.6849 - 3.33833 x1 + 1.780075 x2

Question 8.8. A multiple regression analysis produced the following tables:

Predictor Coefficients Standard Error t Statistic 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: (Points : 2)

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

Question 9.9. 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: (Points : 2)

heated area

number of bedrooms

market value

central heating

residential houses

Question 10.10. In regression analysis, outliers may be identified by examining the: (Points : 2)

coefficient of determination

coefficient of correlation

p-values for the partial coefficients

residuals

R-squared value

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