A cost accountant is developing a regression model to

Question

Assignment

Question 1
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
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
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
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
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
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
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
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
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
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.