A+ Answers
Question 1. 1. According to the following graphic, X and Y have _________.
(Points : 3)
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 : 3)
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 : 3)
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 : 3)
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 0sQuestion 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 : 3)
2090
3850
7400
6950
5150Question 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 : 3)
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-valueIntercept
The regression equation for this analysis is ____________. (Points : 3)
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 |
|
|
|
These results indicate that ____________. (Points : 3)
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 : 3)
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 : 3)
coefficient of determination
coefficient of correlation
p-values for the partial coefficients
residuals
R-squared value
