# 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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