# 5 what criteria are available in determining which of two alternate cost function es 4302840

5) What criteria are available in determining which of two alternate cost function estimates is better for a particular management decision?

A) goodness of fit

B) economic plausibility

C) the significance of the difference between the costs associated with the highest and lowest observations of the cost driver

D) the significance of the difference between the unit values for the highest and lowest observations of the cost driver

E) the goodness of fit and economic plausibility

6) Which of the following is/are one of the criteria used when a manager evaluates an estimated cost function for decision making purposes?

A) economic plausibility

B) goodness of fit

C) irrelevant high-low outliers

D) specifications analysis

E) economic plausibility and goodness of fit

7) In multiple regression, when two or more independent variables are correlated with one another, the situation is known as

A) heteroscedasticity.

B) homoscedasticity.

C) spurious correlation.

D) autocorrelation.

E) multicollinearity.

8) Multicollinearity exists when which of the following conditions is present?

A) At least two variables change due to changes in the cost driver.

B) There are at least two cost pools (usually separated for fixed and variable costs).

C) The underlying value of the coefficient can only be explained in relation to dependent variables.

D) There are two or more statistically significant observations of at least two independent variables.

E) Two or more independent variables are highly correlated with each other.

9) In regression analysis, the term independence of residuals means

A) the residual term for any one observation is not related to the residual term of any other observation.

B) the data exhibit serial correlation.

C) the data exhibit autocorrelation.

D) there is a systematic pattern of positive residuals.

E) there is a systematic pattern of either only positive or only negative residuals.

10) Larson&#39;s Stables uses two different independent variables in two different equations to evaluate the cost activities of training horses, trainer&#39;s hours, and number of horses. The most recent year&#39;s results of the two regressions are as follows:

Trainer&#39;s hours:

 Variable Coefficient Standard Error t-Value Constant 913.32 198.12 4.61 Predictor Variable 20.90 2.94 7.11

r2 = 0.56

Number of horses:

 Variable Coefficient Standard Error t-Value Constant 4,764.50 1,073.09 4.44 Predictor Variable 864.98 247.14 3.50

r2 = 0.63

What is the estimated cost for the coming year if 16,000 trainer hours are incurred and the stable has 400 horses to be trained based on the best cost driver?

A) \$33,555.50

B) \$99,929.09

C) \$350,756.50

D) \$335,313.32

E) \$13,844,444.50

11) C. M. Chain was to manufacture 1,000 chain saws next month. Its accountant has provided the following analysis of the total manufacturing costs.

 Variable Coefficient Standard Error t-Value Constant 200 143.88 1.39 Predictor Variable 400 183.49 2.18

r2 = 0.71

What is the estimated cost of producing the 1,000 chain saws?

A) \$400,200

B) \$284,142

C) \$200,400

D) \$18,000

E) \$9,000

12) Goodness-of-fit measures how well the predicted values in a cost estimating equation

A) match the cost driver.

B) match the actual cost observations.

C) fit the coefficient of determination.

D) rely on the independent variable.

E) rely on the dependent variable

13) Which of the following statements about a high correlation between two variables s and t is FALSE?

A) s may cause t.

B) t may cause s.

C) They both may be affected by a third variable.

D) The correlation establishes an economically plausible relationship between costs and their cost drivers.

E) The correlation may be due to random chance.

Use the information below to answer the following question(s).

Bernie Company used regression analysis to predict the annual cost of indirect materials. The results were as follows:

Indirect Materials Cost Explained by Units Produced

 Constant \$4,378 Standard error of Y estimate \$912 R – squared 0.9183 No. of observations 12 Degrees of freedom 10 X coefficient 2.35 Standard error of coefficient(s) 0.437525

14) The linear cost function is

A) Y =.\$918 + 0.44X.

B) Y = \$912 + \$1.03X.

C) Y = \$4,020 + \$0.92X.

D) Y = \$4,378+ \$0.92X.

E) Y = \$4,378 + \$2.35X.