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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 pvalue
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 5.83E05
Source df SS MS F pvalue
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 pvalue
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 pvalue
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
pvalues for the partial coefficients
residuals
Rsquared value
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