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According to the following graphic, X and Y have _________. (Points :

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
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 : 4)
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


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
2.231711
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 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 : 4)
heated area
number of bedrooms
market value
central heating
residential houses


10. In regression analysis, outliers may be identified by examining the ________. (Points : 4)
coefficient of determination
coefficient of correlation
p-values for the partial coefficients
residuals
R-squared value
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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...

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