Deck 5: Cost Estimation

Question 1

A basic assumption of most cost estimation methods is cost behavior patterns are linear within the relevant range.

Answer 1

True

Question 2

The linear cost estimate tends to understate the slope of the cost line in ranges close to capacity.

Answer 2

True

Question 3

The relevant range represents those activity levels for which valid cost relationships have been observed.

Answer 3

True

Question 4

Because outliers are extreme data points, they can be included in the regression analysis and not significantly affect the results.

Question 5

In general, the account analysis method focuses on the underlying relationship between cost and activities from the previous period.

Question 6

In general, cost behavior results are likely to differ between the engineering method and the account analysis method.

Question 7

The engineering method of determining cost behavior is particularly useful for new activities or products.

Question 8

Cost behavior is the most important characteristic for managerial decision making.

Question 9

A scattergraph is useful for identifying outliers/irrelevant data points.

Question 10

One way to control the effects of a nonlinear relation between total costs and volume is to reduce the relevant range.

Question 11

In general, accounting records accumulate cost information according to its behavior (i.e., variable and fixed).

Question 12

One disadvantage of the high-low method is the highest and lowest points may not be representative of normal operating activities.

Question 13

One advantage of the engineering method is that it does not require data from prior periods to estimate cost behavior.

Question 14

Different cost estimations methods may produce different cost equations, even when using the same set of data.

Question 15

Cost estimates using regression analysis are always more accurate and dependable than cost estimates using the scattergraph methods.

Question 16

The quality of the cost equation depends on collecting appropriate data.

Question 17

The account analysis method is more subjective than other cost estimation methods because it relies heavily on the personal judgment and experience of accountants.

Question 18

In general, the use of multiple independent variables increases the proportion of the variation in the dependent variable explained by the cost equation.

Question 19

One advantage of the account analysis method for estimating cost behavior is that it includes actual work conditions.

Question 20

One advantage that regression techniques have over other cost estimation methods is it generates information that can be used to determine how well the estimated cost equation will predict future costs.

Question 21

Which of the following is not true of regression techniques for estimating costs?

A) They permit the inclusion of more than one predictor.
B) They typically use the highest and lowest activity points to estimate the relation between cost and activity.
C) They help develop estimates that have a broader base than those based on a few select points.
D) They are designed to generate a line that best fits a set of data points.

Question 22

Which of the following statements is(are) true regarding cost behaviors?
(A) In general, accounting records accumulate cost information according to its behavior.
(B) Cost behaviors are the most important consideration in managerial decision making.

A) Only A is true.
B) Only B is true.
C) Both of these are true.
D) Neither of these is true.

Question 23

Which of the following costs would most likely be classified as variable, assuming the account analysis method is used to determine cost behaviors?

A) Indirect materials.
B) Supervisory salaries.
C) Equipment maintenance.
D) Building occupancy costs.

Question 24

The term "relevant range," as used in cost accounting, means the range over which:

A) relevant costs are incurred.
B) costs may fluctuate.
C) cost relationships are valid.
D) cost data is available.

Question 25

Which of the following is the difference between variable costs and fixed costs? (CMA adapted)

A) Variable costs per unit fluctuate and fixed costs per unit remain constant.
B) Variable costs per unit are fixed over the relevant range and fixed costs per unit are variable.
C) Total variable costs are variable over the relevant range and fixed in the long term, while fixed costs never change.
D) Variable costs per unit change in varying increments, while fixed costs per unit change in equal units.

Question 26

The correlation coefficient is:

A) the range of values over which the probability may be estimated based upon the regression equation results.
B) the proportion of the total variance in the dependent variable explained by the independent variable.
C) the measure of variability of the actual observations from the predicting (forecasting) equation line.
D) the relative degree that changes in one variable can be used to estimate changes in another variable.

Question 27

Which of the following cost estimation methods is based on two cost observations?

A) Engineering approach.
B) High-low method.
C) Account analysis.
D) Linear regression.

Question 28

Ballard Company incurred a total cost of $8,600 to produce 400 units of pulp. Each unit of pulp required five (5) direct labor hours to complete. What is the total fixed cost if the variable cost was $1.50 per direct labor hour?

A) $1,700.
B) $3,000.
C) $5,600.
D) $8,000.

Question 29

Brewsky's is a chain of micro-breweries. Managers are interested in the costs of the stores and believe that the costs can be explained in large part by the number of customers patronizing the stores. Monthly data regarding customer visits and costs for the preceding year for one of the stores have been entered into the regression analysis and the analysis is as follows:
$\begin{array}{llr} \text { Average monthly customer visits } &&1,462\\ \text { Average monthly total costs } &&\$4,629\\& \text { Regression Results} &\\ \text { Intercept} &&\$1,496\\ \text { \( b \) coefficient} &&\$2.08\\ \text { R } ^{2}&&0.86814\\ \end{array}$


-
In a regression equation expressed as y = a + bx, how is the letter b best described? (CMA adapted)

A) An estimate of the probability of return customers.
B) The fixed costs per customer visit.
C) The estimate of the cost for an additional customer visit.
D) The proximity of the data points to the regression line.

Question 30

Given the following information, compute the total number of units for the period:
$$\begin{array} { l l} \text { Direct labor hours } & { 12,000 } \\\text { Direct labor cost } & \$ 2.70 \text { per hour } \\\text { Direct materials cost } & \$ 75 \text { per unit } \\\text { Total maruffacturing cost } & \$ 132,600 \\\text { Fixed overhead cost } & \$ 36,000\\\text { Variahle owerhead cost }&\text { \( 500 \% \) of total labor cost}\end{array}$$

A) 360.
B) 432.
C) 640.
D) 840.

Question 31

Which cost estimation method does not use the company's cost information as its primary source of information about the relationship between total costs and activity levels?

A) Scattergraph.
B) High-low.
C) Account analysis.
D) Engineering estimates.

Question 32

In the cost equation TC = F + VX, "V" is best described as the:

A) total costs that do not vary with changes in the activity level.
B) intercept of the cost equation.
C) slope of the cost equation.
D) activity level used to estimate the dependent variable.

Question 33

A manager is trying to estimate the manufacturing costs of a new product. The company makes several other products that utilize some of the same manufacturing procedures as the new product. Which cost estimation method would be the best method to determine the total cost of manufacturing the new product?

A) Engineering estimates.
B) Regression analysis.
C) Account analysis.
D) Scattergraph.

Question 34

Identifying the relation between the activity and the costs is a key step in which of the following cost estimation methods?

A) Scattergraph.
B) High-low method.
C) Account analysis.
D) Linear regression.

Question 35

Obtaining regression estimates for cost estimation requires establishing the existence of a logical relation between activities and the cost to be estimated. Which of the following is not used to refer to the cost to be estimated?

A) left-hand side (LHS).
B) dependent variable.
C) Y term.
D) independent variable.

Question 36

In the cost equation TC = F + VX, "X" is best described as the:

A) costs that do not vary with changes in the activity level.
B) costs that do vary with changes in the activity level.
C) total cost estimate at a particular activity level.
D) activity level used to estimate the total cost.

Question 37

Obtaining regression estimates for cost estimation requires establishing the existence of a logical relation between activities and the cost to be estimated. Which of the following is not used to refer to these activities?

A) independent variables.
B) predictors.
C) dependent variables.
D) X terms.

Question 38

Engineering cost estimates are usually based on operating conditions that are considered:

A) optimal.
B) practical.
C) attainable.
D) historical.

Question 39

A disadvantage of the high-low method of cost analysis is that it:

A) typically results in a totally inaccurate cost formula.
B) is too time consuming to apply.
C) uses only two data points, which may not be representative of normal conditions.
D) relies totally on the judgment of the person performing the cost analysis.

Question 40

A cost driver is defined as: (CMA adapted)

A) the largest cost in a manufacturing process.
B) a fixed cost that cannot be avoided.
C) the significant factor in developing a new product.
D) a causal factor that increases the total cost of a cost objective.

Question 41

The College of Business at Northeast College is accumulating data as a first step in the preparation of next year's budget development. One cost that is being looked at closely is administrative costs as a function of student credit hours. Data on administrative costs and credit hours for the past thirteen months are shown below:
$\begin{array}{lrr}& \text { Administrative } & \text { Credit } \\\text { Month } & \text { Costs } & \text { Hours }\\\text { July } & \$ 129,301 & 250 \\\text { August } & 82,613 & 115 \\\text { September } & 225,580 & 1,392 \\\text { October } & 216,394 & 1,000 \\\text { November } & 258,263 & 1,309\\\text { December } & 184,445 & 1,112 \\\text { January } & 219,137 & 1,335 \\\text { February } & 245,000 & 1,373 \\\text { March } & 209,462 & 1,064 \\\text { April } & 191,925 & 1,123 \\\text { May } & 249,978 & 1,360\\\text { June } & 170,41 \varepsilon & 420 \\\text { July } & 128,167 & 315 \\\text { Total } & \$ 2,510,687 & 12,172 \\\text { Average } & \$ 193,130 & 936\end{array}$

The controller's office has analyzed the data and has given you the results from the regression analysis:
$$\begin{array}{c} { \text { SUMMARY OUTPUT } } \\ { \text { Regression Statistics } } \\\begin{array} { | l | r | } \hline \text { Multiple R } & 0.9317157 \\\hline \text { R Square } & 0.868094147 \\\hline \text { Adjusted R Square } & 0.856102705 \\\hline \text { Standard Error } & 20,134.92395 \\\hline \text { Observations } & 13 \\\hline\end{array}\end{array}$$
$$\begin{array} { | l | r | r | r | r | r | } \hline \text { ANOVA } & & & & & \\\hline & \text { df} & \text { S S } & \text { M S } & \text { F } & \text { Significance } \text { F } \\\hline \text { Repression } & 1 & 29,349,143,514 & 29,349,143,514 & 72.3928117 & 3.61909 \mathrm { E } - 06 \\\hline \text { Residual } & 11 & 4,459,566,787 & 405,415,162.4 & & \\\hline \text { Total } & 12 & 33,808,710,301 & & & \\\hline\end{array}$$


-
If the controller uses the high-low method to estimate costs, the fixed cost portion of the cost equation for administrative costs is:

A) $198,808.00.
B) $69,731.68.
C) $96,409.42.
D) $19,943.58.

Question 42

Brewsky's is a chain of micro-breweries. Managers are interested in the costs of the stores and believe that the costs can be explained in large part by the number of customers patronizing the stores. Monthly data regarding customer visits and costs for the preceding year for one of the stores have been entered into the regression analysis and the analysis is as follows:
$\begin{array}{llr} \text { Average monthly customer visits } &&1,462\\ \text { Average monthly total costs } &&\$4,629\\& \text { Regression Results} &\\ \text { Intercept } &&\$1,496\\ \text { b coefficient } &&\$2.08\\ \text { R }^{2} &&0.86814\\\end{array}$


-
What is the percent of the total variance that can be explained by the regression equation? (CMA adapted)

A) 86.8%
B) 31.6%
C) 97.7%
D) 71.9%

Question 43

The McGraw Company is accumulating data to be used in preparing its annual profit plan for the coming year. The cost behavior pattern of the maintenance costs must be determined. The accounting staff has suggested that linear regression be employed to derive an equation in the form of y = a + bx for maintenance costs. Data regarding the maintenance hours and costs for last year and the results of the regression analysis are as follows: (CMA adapted) $$\begin{array} { l r c } & \begin{array} { c } \text { Hours of } \\\text { Activity }\end{array} & \begin{array} { c } \text { Maintenance } \\\text { Costs }\end{array} \\\text { January } & 480 & \$ 4,200 \\\text { February } & 320 & 3,000 \\ \text { March } & 400 & 3,600 \\\text { April }& 300 & 2,820 \\\text { May } & 500 & 4,350 \\\text { June }& 310 & 2,960 \\\text { July } & 320 & 3,030 \\\text { August } & 520 & 4,470 \\\text { September } & 490 & 4,260 \\\text { October } & 470 & 4,050 \\\text { November } & 350 & 3,300 \\\text { December }& 340 & 3,160 \\ \text { Sum } & 4,800 & \$ 3,200 \\ \text { Average }& 400 & 3,600 \\\text { A coefficient } & & 684.65 \\\text { B coefficient } & & 7.2884 \\\text { Standard error of the a coefficient } & 49.515 & \\\text { Standard error of the b coefficient } & 0.12126 & \\\text { Standard error of the estimate } & & 34.469 \\\mathrm { R } ^ { 2 } & & 0.99724 \\\text { T-value a } & & 13.827 \\\text { T-value b } & & 60.105\end{array}$$

-Based upon the data derived from the regression analysis, 420 maintenance hours in a month would mean the maintenance costs would be budgeted at: (rounded to the nearest whole dollar)

A) $3,797.
B) $3,780.
C) $3,746.
D) $3,600.

Question 44

Fromme's Frocks has the following machine hours and production costs for the last six months of last year:
$\begin{array}{lrr}&\text { Machine }\\\text {Month}&\text {Hours}&\text {Production Coption}\\\text { July } & 15,000 & \$ 12,075 \\\text { August } & 13,500 & 10,800\\\text { September } & 11,500& 9,580\\\text { October } & 15,500 & 12,080\\\text { November } & 14,800& 11,692 \\\text { December } & 12,100 & 9,922\end{array}$

If Fromme expects to incur 14,000 machine hours in January, what will be the estimated total production cost using the high-low method?

A) $8,750.00.
B) $11,142.50.
C) $22,400.00.
D) $10,889.10.

Question 45

The controller of Fortnight Co. has requested a quick estimate of the manufacturing supplies needed for the Cleveland Plant for the month of July, when production is expected to be 470,000 units to meet the ending inventory requirements and sales of 475,000 units. Fortnight Co.'s budget analyst has the following actual data for the last three months.
$\begin{array}{llr}&\text { Production in }\\\text {Month}&\text {Units}&\text { Manufacturing Supplies}\\\text { March } & 450,000 & \$ 723,060\\\text { April } & 540,000 & 853,560\\\text { May } & 480,000 & 766,560\end{array}$

Using the high-low method to develop a cost estimating equation, the total estimated cost of needed manufacturing supplies for July would be: (CMA adapted)

A) $681,500.
B) $688,750.
C) $749,180.
D) $752,060.

Question 46

The McGraw Company is accumulating data to be used in preparing its annual profit plan for the coming year. The cost behavior pattern of the maintenance costs must be determined. The accounting staff has suggested that linear regression be employed to derive an equation in the form of y = a + bx for maintenance costs. Data regarding the maintenance hours and costs for last year and the results of the regression analysis are as follows: (CMA adapted) $$\begin{array} { l r c } & \begin{array} { c } \text { Hours of } \\\text { Activity }\end{array} & \begin{array} { c } \text { Maintenance } \\\text { Costs }\end{array} \\\text { January } & 480 & \$ 4,200 \\\text { February } & 320 & 3,000 \\ \text { March } & 400 & 3,600 \\\text { April }& 300 & 2,820 \\\text { May } & 500 & 4,350 \\\text { June }& 310 & 2,960 \\\text { July } & 320 & 3,030 \\\text { August } & 520 & 4,470 \\\text { September } & 490 & 4,260 \\\text { October } & 470 & 4,050 \\\text { November } & 350 & 3,300 \\\text { December }& 340 & 3,160 \\ \text { Sum } & 4,800 & \$ 3,200 \\ \text { Average }& 400 & 3,600 \\\text { A coefficient } & & 684.65 \\\text { B coefficient } & & 7.2884 \\\text { Standard error of the a coefficient } & 49.515 & \\\text { Standard error of the b coefficient } & 0.12126 & \\\text { Standard error of the estimate } & & 34.469 \\\mathrm { R } ^ { 2 } & & 0.99724 \\\text { T-value a } & & 13.827 \\\text { T-value b } & & 60.105\end{array}$$

- What is the fixed cost per month using the high-low method to estimate the cost equation?

A) $570.
B) $600.
C) $1,140.
D) $2,250.

Question 47

The Crater Manufacturing Company recorded overhead costs of $14,182 at an activity level of 4,200 machine hours and $8,748 at 2,300 machine hours. The records also indicated that overhead of $9,730 was incurred at 2,600 machine hours. What is the variable cost per machine hour using the high-low method to estimate the cost equation?

A) $2.78.
B) $2.86.
C) $3.10.
D) $3.38.

Question 48

Brewsky's is a chain of micro-breweries. Managers are interested in the costs of the stores and believe that the costs can be explained in large part by the number of customers patronizing the stores. Monthly data regarding customer visits and costs for the preceding year for one of the stores have been entered into the regression analysis and the analysis is as follows:
$\begin{array}{llr} \text { Average monthly customer visits } &&1,462\\ \text { Average monthly total costs } &&\$4,629\\& \text { Regression Results} &\\ \text { Intercept } &&\$1,496\\ \text { b coefficient } &&\$2.08\\ \text { R }^{2} &&0.86814\\\end{array}$


-
Based on the data derived from the regression analysis, what are the estimated costs for 1,600 customer visits in a month? (CMA adapted)

A) $6,125.
B) $4,629.
C) $3,328.
D) $4,824.

Question 49

Brewsky's is a chain of micro-breweries. Managers are interested in the costs of the stores and believe that the costs can be explained in large part by the number of customers patronizing the stores. Monthly data regarding customer visits and costs for the preceding year for one of the stores have been entered into the regression analysis and the analysis is as follows:
$\begin{array}{llr} \text { Average monthly customer visits } &&1,462\\ \text { Average monthly total costs } &&\$4,629\\& \text { Regression Results} &\\ \text { Intercept } &&\$1,496\\ \text { b coefficient } &&\$2.08\\ \text { R }^{2} &&0.86814\\\end{array}$


-
In a regression equation expressed as y = a + bx, how is the letter y best described? (CMA adapted)

A) An estimate of the total customers for the month.
B) The observed store cost for a given month.
C) The estimate of the number of new customer visits for the month.
D) The proximity of the data points to the regression line.

Question 50

The McGraw Company is accumulating data to be used in preparing its annual profit plan for the coming year. The cost behavior pattern of the maintenance costs must be determined. The accounting staff has suggested that linear regression be employed to derive an equation in the form of y = a + bx for maintenance costs. Data regarding the maintenance hours and costs for last year and the results of the regression analysis are as follows: (CMA adapted) $$\begin{array} { l r c } & \begin{array} { c } \text { Hours of } \\\text { Activity }\end{array} & \begin{array} { c } \text { Maintenance } \\\text { Costs }\end{array} \\\text { January } & 480 & \$ 4,200 \\\text { February } & 320 & 3,000 \\ \text { March } & 400 & 3,600 \\\text { April }& 300 & 2,820 \\\text { May } & 500 & 4,350 \\\text { June }& 310 & 2,960 \\\text { July } & 320 & 3,030 \\\text { August } & 520 & 4,470 \\\text { September } & 490 & 4,260 \\\text { October } & 470 & 4,050 \\\text { November } & 350 & 3,300 \\\text { December }& 340 & 3,160 \\ \text { Sum } & 4,800 & \$ 3,200 \\ \text { Average }& 400 & 3,600 \\\text { A coefficient } & & 684.65 \\\text { B coefficient } & & 7.2884 \\\text { Standard error of the a coefficient } & 49.515 & \\\text { Standard error of the b coefficient } & 0.12126 & \\\text { Standard error of the estimate } & & 34.469 \\\mathrm { R } ^ { 2 } & & 0.99724 \\\text { T-value a } & & 13.827 \\\text { T-value b } & & 60.105\end{array}$$

- What would be the cost equation if the high-low method is used?

A) Maintenance Costs = $9.00 × Hours of Activity.
B) Maintenance Costs = 3,600 + (400 × Hours of Activity).
C) Maintenance Costs = $570 + ($7.50 × Hours of Activity).
D) Maintenance Costs = $34.469 + ($0.99724 × Hours of Activity).

Question 51

The College of Business at Northeast College is accumulating data as a first step in the preparation of next year's budget development. One cost that is being looked at closely is administrative costs as a function of student credit hours. Data on administrative costs and credit hours for the past thirteen months are shown below:
$\begin{array}{lrr}& \text { Administrative } & \text { Credit } \\\text { Month } & \text { Costs } & \text { Hours }\\\text { July } & \$ 129,301 & 250 \\\text { August } & 82,613 & 115 \\\text { September } & 225,580 & 1,392 \\\text { October } & 216,394 & 1,000 \\\text { November } & 258,263 & 1,309\\\text { December } & 184,445 & 1,112 \\\text { January } & 219,137 & 1,335 \\\text { February } & 245,000 & 1,373 \\\text { March } & 209,462 & 1,064 \\\text { April } & 191,925 & 1,123 \\\text { May } & 249,978 & 1,360\\\text { June } & 170,41 \varepsilon & 420 \\\text { July } & 128,167 & 315 \\\text { Total } & \$ 2,510,687 & 12,172 \\\text { Average } & \$ 193,130 & 936\end{array}$
The controller's office has analyzed the data and has given you the results from the regression analysis:
$$\begin{array}{c} { \text { SUMMARY OUTPUT } } \\ { \text { Regression Statistics } } \\\begin{array} { | l | r | } \hline \text { Multiple R } & 0.9317157 \\\hline \text { R Square } & 0.868094147 \\\hline \text { Adjusted R Square } & 0.856102705 \\\hline \text { Standard Error } & 20,134.92395 \\\hline \text { Observations } & 13 \\\hline\end{array}\end{array}$$
$$\begin{array} { | l | r | r | r | r | r | } \hline \text { ANOVA } & & & & & \\\hline & \text { df} & \text { S S } & \text { M S } & \text { F } & \text { Significance } \text { F } \\\hline \text { Repression } & 1 & 29,349,143,514 & 29,349,143,514 & 72.3928117 & 3.61909 \mathrm { E } - 06 \\\hline \text { Residual } & 11 & 4,459,566,787 & 405,415,162.4 & & \\\hline \text { Total } & 12 & 33,808,710,301 & & & \\\hline\end{array}$$


-
If the controller uses the high-low method to estimate costs, the variable cost per credit hour is:

A) $82.33.
B) $103.56.
C) $111.96.
D) $201.22.

Question 52

The McGraw Company is accumulating data to be used in preparing its annual profit plan for the coming year. The cost behavior pattern of the maintenance costs must be determined. The accounting staff has suggested that linear regression be employed to derive an equation in the form of y = a + bx for maintenance costs. Data regarding the maintenance hours and costs for last year and the results of the regression analysis are as follows: (CMA adapted) $$\begin{array} { l r c } & \begin{array} { c } \text { Hours of } \\\text { Activity }\end{array} & \begin{array} { c } \text { Maintenance } \\\text { Costs }\end{array} \\\text { January } & 480 & \$ 4,200 \\\text { February } & 320 & 3,000 \\ \text { March } & 400 & 3,600 \\\text { April }& 300 & 2,820 \\\text { May } & 500 & 4,350 \\\text { June }& 310 & 2,960 \\\text { July } & 320 & 3,030 \\\text { August } & 520 & 4,470 \\\text { September } & 490 & 4,260 \\\text { October } & 470 & 4,050 \\\text { November } & 350 & 3,300 \\\text { December }& 340 & 3,160 \\ \text { Sum } & 4,800 & \$ 3,200 \\ \text { Average }& 400 & 3,600 \\\text { A coefficient } & & 684.65 \\\text { B coefficient } & & 7.2884 \\\text { Standard error of the a coefficient } & 49.515 & \\\text { Standard error of the b coefficient } & 0.12126 & \\\text { Standard error of the estimate } & & 34.469 \\\mathrm { R } ^ { 2 } & & 0.99724 \\\text { T-value a } & & 13.827 \\\text { T-value b } & & 60.105\end{array}$$

-What is the variable cost per hour using the high-low method to estimate the cost equation?

A) $9.00.
B) $7.50.
C) $0.1333.
D) $0.1111.

Question 53

The McGraw Company is accumulating data to be used in preparing its annual profit plan for the coming year. The cost behavior pattern of the maintenance costs must be determined. The accounting staff has suggested that linear regression be employed to derive an equation in the form of y = a + bx for maintenance costs. Data regarding the maintenance hours and costs for last year and the results of the regression analysis are as follows: (CMA adapted) $$\begin{array} { l r c } & \begin{array} { c } \text { Hours of } \\\text { Activity }\end{array} & \begin{array} { c } \text { Maintenance } \\\text { Costs }\end{array} \\\text { January } & 480 & \$ 4,200 \\\text { February } & 320 & 3,000 \\ \text { March } & 400 & 3,600 \\\text { April }& 300 & 2,820 \\\text { May } & 500 & 4,350 \\\text { June }& 310 & 2,960 \\\text { July } & 320 & 3,030 \\\text { August } & 520 & 4,470 \\\text { September } & 490 & 4,260 \\\text { October } & 470 & 4,050 \\\text { November } & 350 & 3,300 \\\text { December }& 340 & 3,160 \\ \text { Sum } & 4,800 & \$ 3,200 \\ \text { Average }& 400 & 3,600 \\\text { A coefficient } & & 684.65 \\\text { B coefficient } & & 7.2884 \\\text { Standard error of the a coefficient } & 49.515 & \\\text { Standard error of the b coefficient } & 0.12126 & \\\text { Standard error of the estimate } & & 34.469 \\\mathrm { R } ^ { 2 } & & 0.99724 \\\text { T-value a } & & 13.827 \\\text { T-value b } & & 60.105\end{array}$$

-What would be the cost equation if regression analysis is used?

A) Maintenance Costs = $7.2884 + ($684.65 × Hours of Activity).
B) Maintenance Costs = $684.65 + ($49.515 × Hours of Activity).
C) Maintenance Costs = $684.65 + ($7.2884 × Hours of Activity).
D) Maintenance Costs = $34.469 + ($0.99724 × Hours of Activity).

Question 54

The Missou Manufacturing Company recorded overhead costs of $14,182 at an activity level of 4,200 machine hours and $8,748 at 2,300 machine hours. What is the total estimated cost for 2,600 machine hours using the high-low method to estimate the cost equation?

A) $9,730.
B) $9,606.
C) $9,106.
D) $8,788.

Question 55

Brewsky's is a chain of micro-breweries. Managers are interested in the costs of the stores and believe that the costs can be explained in large part by the number of customers patronizing the stores. Monthly data regarding customer visits and costs for the preceding year for one of the stores have been entered into the regression analysis and the analysis is as follows:
$\begin{array}{llr} \text { Average monthly customer visits } &&1,462\\ \text { Average monthly total costs } &&\$4,629\\& \text { Regression Results} &\\ \text { Intercept } &&\$1,496\\ \text { b coefficient } &&\$2.08\\ \text { R }^{2} &&0.86814\\\end{array}$


-
In a regression equation expressed as y = a + bx, how is the letter x best described? (CMA adapted)

A) Fixed costs per each customer-visit.
B) The observed store costs for a given month.
C) The estimate of the number of new customer visits for the month.
D) The observed customer visits for a given month.

Question 56

The College of Business at Northeast College is accumulating data as a first step in the preparation of next year's budget development. One cost that is being looked at closely is administrative costs as a function of student credit hours. Data on administrative costs and credit hours for the past thirteen months are shown below:
$\begin{array}{lrr}& \text { Administrative } & \text { Credit } \\\text { Month } & \text { Costs } & \text { Hours }\\\text { July } & \$ 129,301 & 250 \\\text { August } & 82,613 & 115 \\\text { September } & 225,580 & 1,392 \\\text { October } & 216,394 & 1,000 \\\text { November } & 258,263 & 1,309\\\text { December } & 184,445 & 1,112 \\\text { January } & 219,137 & 1,335 \\\text { February } & 245,000 & 1,373 \\\text { March } & 209,462 & 1,064 \\\text { April } & 191,925 & 1,123 \\\text { May } & 249,978 & 1,360\\\text { June } & 170,41 \varepsilon & 420 \\\text { July } & 128,167 & 315 \\\text { Total } & \$ 2,510,687 & 12,172 \\\text { Average } & \$ 193,130 & 936\end{array}$

The controller's office has analyzed the data and has given you the results from the regression analysis:
$$\begin{array}{c} { \text { SUMMARY OUTPUT } } \\ { \text { Regression Statistics } } \\\begin{array} { | l | r | } \hline \text { Multiple R } & 0.9317157 \\\hline \text { R Square } & 0.868094147 \\\hline \text { Adjusted R Square } & 0.856102705 \\\hline \text { Standard Error } & 20,134.92395 \\\hline \text { Observations } & 13 \\\hline\end{array}\end{array}$$
$$\begin{array} { | l | r | r | r | r | r | } \hline \text { ANOVA } & & & & & \\\hline & \text { df} & \text { S S } & \text { M S } & \text { F } & \text { Significance } \text { F } \\\hline \text { Repression } & 1 & 29,349,143,514 & 29,349,143,514 & 72.3928117 & 3.61909 \mathrm { E } - 06 \\\hline \text { Residual } & 11 & 4,459,566,787 & 405,415,162.4 & & \\\hline \text { Total } & 12 & 33,808,710,301 & & & \\\hline\end{array}$$


-
If the controller uses the high-low method to estimate costs, the cost equation for administrative costs is

A) Administrative Costs = $96,409.42 + $103.56 × Credit-hours.
B) Administrative Costs = $69,731.68 + $111.96 × Credit-hours.
C) Administrative Costs = $201.21 × Credit-hours.
D) Administrative Costs = $198,808.

Question 57

The Macon Company uses the high-low method to determine its cost equation. The following information was gathered for the past year:
$$\begin{array} { l c c } & \text { Machine } & \text { Direct Labor } \\ & \text { Hours } & \text { Costs } \\ \text { Busiest month (June) }& 14,000 & \$ 2 0 0 , 0 0 0 \\\text { Slowest month (December) }& 6,000 & \$ 120,000\end{array}$$

-
If Macon expects to use 10,000 machine hours next month, what are the estimated direct labor costs?

A) $160,000.
B) $180,000.
C) $175,000.
D) $150,000.

Question 58

The Macon Company uses the high-low method to determine its cost equation. The following information was gathered for the past year:
$$\begin{array} { l c c } & \text { Machine } & \text { Direct Labor } \\ & \text { Hours } & \text { Costs } \\ \text { Busiest month (June) }& 14,000 & \$ 2 0 0 , 0 0 0 \\\text { Slowest month (December) }& 6,000 & \$ 120,000\end{array}$$

-
What are the direct labor costs per machine hour?

A) $20.00.
B) $16.00.
C) $14.29.
D) $10.00.

Question 59

The McGraw Company is accumulating data to be used in preparing its annual profit plan for the coming year. The cost behavior pattern of the maintenance costs must be determined. The accounting staff has suggested that linear regression be employed to derive an equation in the form of y = a + bx for maintenance costs. Data regarding the maintenance hours and costs for last year and the results of the regression analysis are as follows: (CMA adapted) $$\begin{array} { l r c } & \begin{array} { c } \text { Hours of } \\\text { Activity }\end{array} & \begin{array} { c } \text { Maintenance } \\\text { Costs }\end{array} \\\text { January } & 480 & \$ 4,200 \\\text { February } & 320 & 3,000 \\ \text { March } & 400 & 3,600 \\\text { April }& 300 & 2,820 \\\text { May } & 500 & 4,350 \\\text { June }& 310 & 2,960 \\\text { July } & 320 & 3,030 \\\text { August } & 520 & 4,470 \\\text { September } & 490 & 4,260 \\\text { October } & 470 & 4,050 \\\text { November } & 350 & 3,300 \\\text { December }& 340 & 3,160 \\ \text { Sum } & 4,800 & \$ 3,200 \\ \text { Average }& 400 & 3,600 \\\text { A coefficient } & & 684.65 \\\text { B coefficient } & & 7.2884 \\\text { Standard error of the a coefficient } & 49.515 & \\\text { Standard error of the b coefficient } & 0.12126 & \\\text { Standard error of the estimate } & & 34.469 \\\mathrm { R } ^ { 2 } & & 0.99724 \\\text { T-value a } & & 13.827 \\\text { T-value b } & & 60.105\end{array}$$

-Using the high-low method to estimate cost behavior, 420 maintenance hours in a month would mean the maintenance costs would be budgeted at:

A) $3,150.
B) $3,600.
C) $3,720.
D) $3,780.

Question 60

The cost accountants at the Barkley Company regressed total overhead costs and direct labor hours for the past 30-months and reported the following results:
$$\begin{array} { l r r } \text { Slope } & \$ 41.27 \\\text { Intercept } & \$ 596.36 \\\text { Correlation Coefficient } & 0.934\end{array}$$
What is the estimated overhead cost if 225 direct labor hours are expected to be used in the upcoming period? (rounded to the nearest whole dollar)

A) $10,534.
B) $9,882.
C) $9,230.
D) $8,617.

Question 61

Thane Company is interested in establishing the relationship between electricity costs and machine hours. Data have been collected and a regression analysis prepared using Excel. The monthly data and the regression output follow:
$\begin{array}{lll}\text {Month}&\text {Machine Hours}&\text {Electricity Costs}\\\text { January } & 2,500 & \$ 18,400 \\\text { February } & 2,900 & 21,000 \\\text { March } & 1,900& 13,500 \\\text { April } & 3,100 & 23,000 \\\text { May } & 3,800& 28,250 \\\text { June } & 3,300& 22,000 \\\text { July } & 4,100 & 24,750\\\text { August } & 3,500 & 22,750 \\\text { September } & 2,000 & 15,500 \\\text { October } & 3,700 & 26,000 \\\text { November } & 4,700 & 31,000 \\\text { December } & 4,200 & 27,750\end{array}$

$$\begin{array}{c} { \text { Summary Output } } \\ { \text { Regression Statistics } } \\\begin{array}{ | l | c | } \hline \text { Multiple R } & 0.965 \\\hline \text { R Squuare } & 0.932 \\\hline \text { Adjusted R } ^2 & 0.925 \\\hline \text { Standard Error } & 1,425.18 \\\hline \text { Observations } & 12.00 \\\hline\end{array}\end{array}$$

$\begin{array}{|l|r|r|r|r|r|r|}\hline && \text { Standard } & && \text { Lower } & \text { Upper } \\&\text { Coefficients } & \text { Error } & \text { t Stat } & \text { P-value } & 95 \% & 95 \% \\ \hline \text { Intercept } & 3,726.88 & 1,682.82 & 2.21 & 0.05 & (22.69) & 7,476.45 \\\hline \text { Machine } & 5.77 & 0.49 & 11.7 & 0.00 & 4.67 & 6.87 \\\text { Hours } & & & & & \\\hline\end{array}$


-
If the controller uses the high-low method to estimate costs, the variable cost per machine hour is:

A) $6.25.
B) $6.90.
C) $5.77.
D) $11.70.

Question 62

The College of Business at Northeast College is accumulating data as a first step in the preparation of next year's budget development. One cost that is being looked at closely is administrative costs as a function of student credit hours. Data on administrative costs and credit hours for the past thirteen months are shown below:
$\begin{array}{lrr}& \text { Administrative } & \text { Credit } \\\text { Month } & \text { Costs } & \text { Hours }\\\text { July } & \$ 129,301 & 250 \\\text { August } & 82,613 & 115 \\\text { September } & 225,580 & 1,392 \\\text { October } & 216,394 & 1,000 \\\text { November } & 258,263 & 1,309\\\text { December } & 184,445 & 1,112 \\\text { January } & 219,137 & 1,335 \\\text { February } & 245,000 & 1,373 \\\text { March } & 209,462 & 1,064 \\\text { April } & 191,925 & 1,123 \\\text { May } & 249,978 & 1,360\\\text { June } & 170,41 \varepsilon & 420 \\\text { July } & 128,167 & 315 \\\text { Total } & \$ 2,510,687 & 12,172 \\\text { Average } & \$ 193,130 & 936\end{array}$


The controller's office has analyzed the data and has given you the results from the regression analysis:
$$\begin{array}{c} { \text { SUMMARY OUTPUT } } \\ { \text { Regression Statistics } } \\\begin{array} { | l | r | } \hline \text { Multiple R } & 0.9317157 \\\hline \text { R Square } & 0.868094147 \\\hline \text { Adjusted R Square } & 0.856102705 \\\hline \text { Standard Error } & 20,134.92395 \\\hline \text { Observations } & 13 \\\hline\end{array}\end{array}$$
$$\begin{array} { | l | r | r | r | r | r | } \hline \text { ANOVA } & & & & & \\\hline & \text { df} & \text { S S } & \text { M S } & \text { F } & \text { Significance } \text { F } \\\hline \text { Repression } & 1 & 29,349,143,514 & 29,349,143,514 & 72.3928117 & 3.61909 \mathrm { E } - 06 \\\hline \text { Residual } & 11 & 4,459,566,787 & 405,415,162.4 & & \\\hline \text { Total } & 12 & 33,808,710,301 & & & \\\hline\end{array}$$



-
Based on the results of the regression analysis, the estimate of the variable portion of administrative costs in a month with 200 credit hours would be:

A) $198,808.
B) $20,612.
C) $117,121.
D) $40,242.

Question 63

Thane Company is interested in establishing the relationship between electricity costs and machine hours. Data have been collected and a regression analysis prepared using Excel. The monthly data and the regression output follow:
$\begin{array}{lll}\text {Month}&\text {Machine Hours}&\text {Electricity Costs}\\\text { January } & 2,500 & \$ 18,400 \\\text { February } & 2,900 & 21,000 \\\text { March } & 1,900& 13,500 \\\text { April } & 3,100 & 23,000 \\\text { May } & 3,800& 28,250 \\\text { June } & 3,300& 22,000 \\\text { July } & 4,100 & 24,750\\\text { August } & 3,500 & 22,750 \\\text { September } & 2,000 & 15,500 \\\text { October } & 3,700 & 26,000 \\\text { November } & 4,700 & 31,000 \\\text { December } & 4,200 & 27,750\end{array}$



$$\begin{array}{c} { \text { Summary Output } } \\ { \text { Regression Statistics } } \\\begin{array}{ | l | c | } \hline \text { Multiple R } & 0.965 \\\hline \text { R Squuare } & 0.932 \\\hline \text { Adjusted R } ^2 & 0.925 \\\hline \text { Standard Error } & 1,425.18 \\\hline \text { Observations } & 12.00 \\\hline\end{array}\end{array}$$

$\begin{array}{|l|r|r|r|r|r|r|}\hline && \text { Standard } & && \text { Lower } & \text { Upper } \\&\text { Coefficients } & \text { Error } & \text { t Stat } & \text { P-value } & 95 \% & 95 \% \\ \hline \text { Intercept } & 3,726.88 & 1,682.82 & 2.21 & 0.05 & (22.69) & 7,476.45 \\\hline \text { Machine } & 5.77 & 0.49 & 11.7 & 0.00 & 4.67 & 6.87 \\\text { Hours } & & & & & \\\hline\end{array}$



-
Based on the results of the high-low analysis, the estimate of electricity costs in a month with 2,200 machine hours would be:

A) $15,375.
B) $22,825.
C) $15,180.
D) $16,427.

Question 64

The College of Business at Northeast College is accumulating data as a first step in the preparation of next year's budget development. One cost that is being looked at closely is administrative costs as a function of student credit hours. Data on administrative costs and credit hours for the past thirteen months are shown below:
$\begin{array}{lrr}& \text { Administrative } & \text { Credit } \\\text { Month } & \text { Costs } & \text { Hours }\\\text { July } & \$ 129,301 & 250 \\\text { August } & 82,613 & 115 \\\text { September } & 225,580 & 1,392 \\\text { October } & 216,394 & 1,000 \\\text { November } & 258,263 & 1,309\\\text { December } & 184,445 & 1,112 \\\text { January } & 219,137 & 1,335 \\\text { February } & 245,000 & 1,373 \\\text { March } & 209,462 & 1,064 \\\text { April } & 191,925 & 1,123 \\\text { May } & 249,978 & 1,360\\\text { June } & 170,41 \varepsilon & 420 \\\text { July } & 128,167 & 315 \\\text { Total } & \$ 2,510,687 & 12,172 \\\text { Average } & \$ 193,130 & 936\end{array}$

The controller's office has analyzed the data and has given you the results from the regression analysis:
$$\begin{array}{c} { \text { SUMMARY OUTPUT } } \\ { \text { Regression Statistics } } \\\begin{array} { | l | r | } \hline \text { Multiple R } & 0.9317157 \\\hline \text { R Square } & 0.868094147 \\\hline \text { Adjusted R Square } & 0.856102705 \\\hline \text { Standard Error } & 20,134.92395 \\\hline \text { Observations } & 13 \\\hline\end{array}\end{array}$$
$$\begin{array} { | l | r | r | r | r | r | } \hline \text { ANOVA } & & & & & \\\hline & \text { df} & \text { S S } & \text { M S } & \text { F } & \text { Significance } \text { F } \\\hline \text { Repression } & 1 & 29,349,143,514 & 29,349,143,514 & 72.3928117 & 3.61909 \mathrm { E } - 06 \\\hline \text { Residual } & 11 & 4,459,566,787 & 405,415,162.4 & & \\\hline \text { Total } & 12 & 33,808,710,301 & & & \\\hline\end{array}$$


-
If the controller uses regression analysis to estimate costs, the cost equation for administrative costs is:

A) Administrative Costs = $19,943.58 + ($13.00 × Credit hours).
B) Administrative Costs = $69,474.40 + ($114.30 × Credit hours).
C) Administrative Costs = $96,647.02 + ($103.06 × Credit hours).
D) Administrative Costs = $12,521.26 + ($11.99 × Credit hours).

Question 65

The College of Business at Northeast College is accumulating data as a first step in the preparation of next year's budget development. One cost that is being looked at closely is administrative costs as a function of student credit hours. Data on administrative costs and credit hours for the past thirteen months are shown below:
$\begin{array}{lrr}& \text { Administrative } & \text { Credit } \\\text { Month } & \text { Costs } & \text { Hours }\\\text { July } & \$ 129,301 & 250 \\\text { August } & 82,613 & 115 \\\text { September } & 225,580 & 1,392 \\\text { October } & 216,394 & 1,000 \\\text { November } & 258,263 & 1,309\\\text { December } & 184,445 & 1,112 \\\text { January } & 219,137 & 1,335 \\\text { February } & 245,000 & 1,373 \\\text { March } & 209,462 & 1,064 \\\text { April } & 191,925 & 1,123 \\\text { May } & 249,978 & 1,360\\\text { June } & 170,41 \varepsilon & 420 \\\text { July } & 128,167 & 315 \\\text { Total } & \$ 2,510,687 & 12,172 \\\text { Average } & \$ 193,130 & 936\end{array}$


The controller's office has analyzed the data and has given you the results from the regression analysis:
$$\begin{array}{c} { \text { SUMMARY OUTPUT } } \\ { \text { Regression Statistics } } \\\begin{array} { | l | r | } \hline \text { Multiple R } & 0.9317157 \\\hline \text { R Square } & 0.868094147 \\\hline \text { Adjusted R Square } & 0.856102705 \\\hline \text { Standard Error } & 20,134.92395 \\\hline \text { Observations } & 13 \\\hline\end{array}\end{array}$$
$$\begin{array} { | l | r | r | r | r | r | } \hline \text { ANOVA } & & & & & \\\hline & \text { df} & \text { S S } & \text { M S } & \text { F } & \text { Significance } \text { F } \\\hline \text { Repression } & 1 & 29,349,143,514 & 29,349,143,514 & 72.3928117 & 3.61909 \mathrm { E } - 06 \\\hline \text { Residual } & 11 & 4,459,566,787 & 405,415,162.4 & & \\\hline \text { Total } & 12 & 33,808,710,301 & & & \\\hline\end{array}$$



-
If the controller uses regression analysis to estimate costs, the estimate of the variable portion of administrative costs is:

A) Variable Costs = $8.63 × Credit hours.
B) Variable Costs = $0.87 × Credit hours.
C) Variable Costs = $103.06 × Credit hours.
D) Variable Costs = $11.99 × Credit hours.

Question 66

In determining cost behavior in business, the cost function is often expressed as Y = a + bX. Which one of the following cost estimation methods should not be used in estimating fixed and variable costs for the equation? (CMA adapted)

A) Scattergraph method.
B) Simple regression.
C) High and low point method.
D) Management analysis of data.

Question 67

Thane Company is interested in establishing the relationship between electricity costs and machine hours. Data have been collected and a regression analysis prepared using Excel. The monthly data and the regression output follow:
$\begin{array}{lll}\text {Month}&\text {Machine Hours}&\text {Electricity Costs}\\\text { January } & 2,500 & \$ 18,400 \\\text { February } & 2,900 & 21,000 \\\text { March } & 1,900& 13,500 \\\text { April } & 3,100 & 23,000 \\\text { May } & 3,800& 28,250 \\\text { June } & 3,300& 22,000 \\\text { July } & 4,100 & 24,750\\\text { August } & 3,500 & 22,750 \\\text { September } & 2,000 & 15,500 \\\text { October } & 3,700 & 26,000 \\\text { November } & 4,700 & 31,000 \\\text { December } & 4,200 & 27,750\end{array}$



$$\begin{array}{c}{ \text { Summary Output } } \\ { \text { Regression Statistic} } \\\begin{array} { | c | c | } \hline \text { Multiple R } & 0.965 \\\hline \text { R Squuare } & 0.932 \\\hline \text { Adjusted R } ^2& 0.925 \\\hline \text { Standard Error } & 1,425.18 \\\hline \text { Observations } & 12.00 \\\hline\end{array}\end{array}$$
$\begin{array}{|l|r|r|r|r|r|r|}\hline && \text { Standard } & && \text { Lower } & \text { Upper } \\&\text { Coefficients } & \text { Error } & \text { t Stat } & \text { P-value } & 95 \% & 95 \% \\ \hline \text { Intercept } & 3,726.88 & 1,682.82 & 2.21 & 0.05 & (22.69) & 7,476.45 \\\hline \text { Machine } & 5.77 & 0.49 & 11.7 & 0.00 & 4.67 & 6.87 \\\text { Hours } & & & & & \\\hline\end{array}$




-
The correlation coefficient for the regression equation for electricity costs is:

A) 0.965.
B) 0.932.
C) 0.925.
D) 0.982.

Question 68

Thane Company is interested in establishing the relationship between electricity costs and machine hours. Data have been collected and a regression analysis prepared using Excel. The monthly data and the regression output follow:
$\begin{array}{lll}\text {Month}&\text {Machine Hours}&\text {Electricity Costs}\\\text { January } & 2,500 & \$ 18,400 \\\text { February } & 2,900 & 21,000 \\\text { March } & 1,900& 13,500 \\\text { April } & 3,100 & 23,000 \\\text { May } & 3,800& 28,250 \\\text { June } & 3,300& 22,000 \\\text { July } & 4,100 & 24,750\\\text { August } & 3,500 & 22,750 \\\text { September } & 2,000 & 15,500 \\\text { October } & 3,700 & 26,000 \\\text { November } & 4,700 & 31,000 \\\text { December } & 4,200 & 27,750\end{array}$



$$\begin{array}{c} { \text { Summary Output } } \\ { \text { Regression Statistics } } \\\begin{array}{ | l | c | } \hline \text { Multiple R } & 0.965 \\\hline \text { R Squuare } & 0.932 \\\hline \text { Adjusted R } ^2 & 0.925 \\\hline \text { Standard Error } & 1,425.18 \\\hline \text { Observations } & 12.00 \\\hline\end{array}\end{array}$$
$\begin{array}{|l|r|r|r|r|r|r|}\hline && \text { Standard } & && \text { Lower } & \text { Upper } \\&\text { Coefficients } & \text { Error } & \text { t Stat } & \text { P-value } & 95 \% & 95 \% \\ \hline \text { Intercept } & 3,726.88 & 1,682.82 & 2.21 & 0.05 & (22.69) & 7,476.45 \\\hline \text { Machine } & 5.77 & 0.49 & 11.7 & 0.00 & 4.67 & 6.87 \\\text { Hours } & & & & & \\\hline\end{array}$




-
The percent of the total variance that can be explained by the regression is:

A) 96.5%.
B) 93.2%.
C) 92.5%.
D) 98.2%.

Question 69

The College of Business at Northeast College is accumulating data as a first step in the preparation of next year's budget development. One cost that is being looked at closely is administrative costs as a function of student credit hours. Data on administrative costs and credit hours for the past thirteen months are shown below:
$\begin{array}{lrr}& \text { Administrative } & \text { Credit } \\\text { Month } & \text { Costs } & \text { Hours }\\\text { July } & \$ 129,301 & 250 \\\text { August } & 82,613 & 115 \\\text { September } & 225,580 & 1,392 \\\text { October } & 216,394 & 1,000 \\\text { November } & 258,263 & 1,309\\\text { December } & 184,445 & 1,112 \\\text { January } & 219,137 & 1,335 \\\text { February } & 245,000 & 1,373 \\\text { March } & 209,462 & 1,064 \\\text { April } & 191,925 & 1,123 \\\text { May } & 249,978 & 1,360\\\text { June } & 170,41 \varepsilon & 420 \\\text { July } & 128,167 & 315 \\\text { Total } & \$ 2,510,687 & 12,172 \\\text { Average } & \$ 193,130 & 936\end{array}$

The controller's office has analyzed the data and has given you the results from the regression analysis:
$$\begin{array}{c} { \text { SUMMARY OUTPUT } } \\ { \text { Regression Statistics } } \\\begin{array} { | l | r | } \hline \text { Multiple R } & 0.9317157 \\\hline \text { R Square } & 0.868094147 \\\hline \text { Adjusted R Square } & 0.856102705 \\\hline \text { Standard Error } & 20,134.92395 \\\hline \text { Observations } & 13 \\\hline\end{array}\end{array}$$
$$\begin{array} { | l | r | r | r | r | r | } \hline \text { ANOVA } & & & & & \\\hline & \text { df} & \text { S S } & \text { M S } & \text { F } & \text { Significance } \text { F } \\\hline \text { Repression } & 1 & 29,349,143,514 & 29,349,143,514 & 72.3928117 & 3.61909 \mathrm { E } - 06 \\\hline \text { Residual } & 11 & 4,459,566,787 & 405,415,162.4 & & \\\hline \text { Total } & 12 & 33,808,710,301 & & & \\\hline\end{array}$$


-
Based on the results of the high-low analysis, the estimate of administrative costs in a month with 1,000 credit hours would be: (rounded to the nearest whole dollar)

A) $181,692.
B) $199,969.
C) $201,210.
D) $198,808.

Question 70

The College of Business at Northeast College is accumulating data as a first step in the preparation of next year's budget development. One cost that is being looked at closely is administrative costs as a function of student credit hours. Data on administrative costs and credit hours for the past thirteen months are shown below:
$\begin{array}{lrr}& \text { Administrative } & \text { Credit } \\\text { Month } & \text { Costs } & \text { Hours }\\\text { July } & \$ 129,301 & 250 \\\text { August } & 82,613 & 115 \\\text { September } & 225,580 & 1,392 \\\text { October } & 216,394 & 1,000 \\\text { November } & 258,263 & 1,309\\\text { December } & 184,445 & 1,112 \\\text { January } & 219,137 & 1,335 \\\text { February } & 245,000 & 1,373 \\\text { March } & 209,462 & 1,064 \\\text { April } & 191,925 & 1,123 \\\text { May } & 249,978 & 1,360\\\text { June } & 170,41 \varepsilon & 420 \\\text { July } & 128,167 & 315 \\\text { Total } & \$ 2,510,687 & 12,172 \\\text { Average } & \$ 193,130 & 936\end{array}$


The controller's office has analyzed the data and has given you the results from the regression analysis:
$$\begin{array}{c} { \text { SUMMARY OUTPUT } } \\ { \text { Regression Statistics } } \\\begin{array} { | l | r | } \hline \text { Multiple R } & 0.9317157 \\\hline \text { R Square } & 0.868094147 \\\hline \text { Adjusted R Square } & 0.856102705 \\\hline \text { Standard Error } & 20,134.92395 \\\hline \text { Observations } & 13 \\\hline\end{array}\end{array}$$
$$\begin{array} { | l | r | r | r | r | r | } \hline \text { ANOVA } & & & & & \\\hline & \text { df} & \text { S S } & \text { M S } & \text { F } & \text { Significance } \text { F } \\\hline \text { Repression } & 1 & 29,349,143,514 & 29,349,143,514 & 72.3928117 & 3.61909 \mathrm { E } - 06 \\\hline \text { Residual } & 11 & 4,459,566,787 & 405,415,162.4 & & \\\hline \text { Total } & 12 & 33,808,710,301 & & & \\\hline\end{array}$$



-
Based on the results of the regression analysis, the estimate of administrative costs in a month with 1,000 credit hours would be: (rounded to the nearest whole dollar)

A) $198,808.
B) $201,000.
C) $199,707.
D) $96,409.

Question 71

Which of the following may be used to estimate how inventory warehouse costs are affected by both the number of shipments and the weight of the material handled? (CPA adapted)

A) Economic order quantity analysis.
B) Probability analysis.
C) Correlation analysis.
D) Multiple regression analysis.

Question 72

The College of Business at Northeast College is accumulating data as a first step in the preparation of next year's budget development. One cost that is being looked at closely is administrative costs as a function of student credit hours. Data on administrative costs and credit hours for the past thirteen months are shown below:
$\begin{array}{lrr}& \text { Administrative } & \text { Credit } \\\text { Month } & \text { Costs } & \text { Hours }\\\text { July } & \$ 129,301 & 250 \\\text { August } & 82,613 & 115 \\\text { September } & 225,580 & 1,392 \\\text { October } & 216,394 & 1,000 \\\text { November } & 258,263 & 1,309\\\text { December } & 184,445 & 1,112 \\\text { January } & 219,137 & 1,335 \\\text { February } & 245,000 & 1,373 \\\text { March } & 209,462 & 1,064 \\\text { April } & 191,925 & 1,123 \\\text { May } & 249,978 & 1,360\\\text { June } & 170,41 \varepsilon & 420 \\\text { July } & 128,167 & 315 \\\text { Total } & \$ 2,510,687 & 12,172 \\\text { Average } & \$ 193,130 & 936\end{array}$


The controller's office has analyzed the data and has given you the results from the regression analysis:
$$\begin{array}{c} { \text { SUMMARY OUTPUT } } \\ { \text { Regression Statistics } } \\\begin{array} { | l | r | } \hline \text { Multiple R } & 0.9317157 \\\hline \text { R Square } & 0.868094147 \\\hline \text { Adjusted R Square } & 0.856102705 \\\hline \text { Standard Error } & 20,134.92395 \\\hline \text { Observations } & 13 \\\hline\end{array}\end{array}$$
$$\begin{array} { | l | r | r | r | r | r | } \hline \text { ANOVA } & & & & & \\\hline & \text { df} & \text { S S } & \text { M S } & \text { F } & \text { Significance } \text { F } \\\hline \text { Repression } & 1 & 29,349,143,514 & 29,349,143,514 & 72.3928117 & 3.61909 \mathrm { E } - 06 \\\hline \text { Residual } & 11 & 4,459,566,787 & 405,415,162.4 & & \\\hline \text { Total } & 12 & 33,808,710,301 & & & \\\hline\end{array}$$



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The percent of the total variance that can be explained by the regression is:

A) 93.3%.
B) 86.8%.
C) 85.9%.
D) 96.6%.

Question 73

Thane Company is interested in establishing the relationship between electricity costs and machine hours. Data have been collected and a regression analysis prepared using Excel. The monthly data and the regression output follow:
$\begin{array}{lll}\text {Month}&\text {Machine Hours}&\text {Electricity Costs}\\\text { January } & 2,500 & \$ 18,400 \\\text { February } & 2,900 & 21,000 \\\text { March } & 1,900& 13,500 \\\text { April } & 3,100 & 23,000 \\\text { May } & 3,800& 28,250 \\\text { June } & 3,300& 22,000 \\\text { July } & 4,100 & 24,750\\\text { August } & 3,500 & 22,750 \\\text { September } & 2,000 & 15,500 \\\text { October } & 3,700 & 26,000 \\\text { November } & 4,700 & 31,000 \\\text { December } & 4,200 & 27,750\end{array}$



$$\begin{array}{c} { \text { Summary Output } } \\ { \text { Regression Statistics } } \\\begin{array}{ | l | c | } \hline \text { Multiple R } & 0.965 \\\hline \text { R Squuare } & 0.932 \\\hline \text { Adjusted R } ^2 & 0.925 \\\hline \text { Standard Error } & 1,425.18 \\\hline \text { Observations } & 12.00 \\\hline\end{array}\end{array}$$


$\begin{array}{|l|r|r|r|r|r|r|}\hline && \text { Standard } & && \text { Lower } & \text { Upper } \\&\text { Coefficients } & \text { Error } & \text { t Stat } & \text { P-value } & 95 \% & 95 \% \\ \hline \text { Intercept } & 3,726.88 & 1,682.82 & 2.21 & 0.05 & (22.69) & 7,476.45 \\\hline \text { Machine } & 5.77 & 0.49 & 11.7 & 0.00 & 4.67 & 6.87 \\\text { Hours } & & & & & \\\hline\end{array}$



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If the controller uses regression analysis to estimate costs, the estimate of the variable portion of electricity costs is:

A) Electricity Costs = $11.70 × Machine hours.
B) Electricity Costs = $0.93 × Machine hours.
C) Electricity Costs = $5.77 × Machine hours.
D) Electricity Costs = $0.49 × Machine hours.

Question 74

Thane Company is interested in establishing the relationship between electricity costs and machine hours. Data have been collected and a regression analysis prepared using Excel. The monthly data and the regression output follow:
$\begin{array}{lll}\text {Month}&\text {Machine Hours}&\text {Electricity Costs}\\\text { January } & 2,500 & \$ 18,400 \\\text { February } & 2,900 & 21,000 \\\text { March } & 1,900& 13,500 \\\text { April } & 3,100 & 23,000 \\\text { May } & 3,800& 28,250 \\\text { June } & 3,300& 22,000 \\\text { July } & 4,100 & 24,750\\\text { August } & 3,500 & 22,750 \\\text { September } & 2,000 & 15,500 \\\text { October } & 3,700 & 26,000 \\\text { November } & 4,700 & 31,000 \\\text { December } & 4,200 & 27,750\end{array}$



$$\begin{array}{c} { \text { Summary Output } } \\ { \text { Regression Statistics } } \\\begin{array}{ | l | c | } \hline \text { Multiple R } & 0.965 \\\hline \text { R Squuare } & 0.932 \\\hline \text { Adjusted R } ^2 & 0.925 \\\hline \text { Standard Error } & 1,425.18 \\\hline \text { Observations } & 12.00 \\\hline\end{array}\end{array}$$

$\begin{array}{|l|r|r|r|r|r|r|}\hline && \text { Standard } & && \text { Lower } & \text { Upper } \\&\text { Coefficients } & \text { Error } & \text { t Stat } & \text { P-value } & 95 \% & 95 \% \\ \hline \text { Intercept } & 3,726.88 & 1,682.82 & 2.21 & 0.05 & (22.69) & 7,476.45 \\\hline \text { Machine } & 5.77 & 0.49 & 11.7 & 0.00 & 4.67 & 6.87 \\\text { Hours } & & & & & \\\hline\end{array}$



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Based on the results of the regression analysis, the estimate of electricity costs in a month with 2,200 machine hours would be: (rounded to the nearest whole dollar)

A) $3,727.
B) $16,421.
C) $15,180.
D) $22,825.

Question 75

Thane Company is interested in establishing the relationship between electricity costs and machine hours. Data have been collected and a regression analysis prepared using Excel. The monthly data and the regression output follow:
$\begin{array}{lll}\text {Month}&\text {Machine Hours}&\text {Electricity Costs}\\\text { January } & 2,500 & \$ 18,400 \\\text { February } & 2,900 & 21,000 \\\text { March } & 1,900& 13,500 \\\text { April } & 3,100 & 23,000 \\\text { May } & 3,800& 28,250 \\\text { June } & 3,300& 22,000 \\\text { July } & 4,100 & 24,750\\\text { August } & 3,500 & 22,750 \\\text { September } & 2,000 & 15,500 \\\text { October } & 3,700 & 26,000 \\\text { November } & 4,700 & 31,000 \\\text { December } & 4,200 & 27,750\end{array}$



$$\begin{array}{c} { \text { Summary Output } } \\ { \text { Regression Statistics } } \\\begin{array}{ | l | c | } \hline \text { Multiple R } & 0.965 \\\hline \text { R Squuare } & 0.932 \\\hline \text { Adjusted R } ^2 & 0.925 \\\hline \text { Standard Error } & 1,425.18 \\\hline \text { Observations } & 12.00 \\\hline\end{array}\end{array}$$


$\begin{array}{|l|r|r|r|r|r|r|}\hline && \text { Standard } & && \text { Lower } & \text { Upper } \\&\text { Coefficients } & \text { Error } & \text { t Stat } & \text { P-value } & 95 \% & 95 \% \\ \hline \text { Intercept } & 3,726.88 & 1,682.82 & 2.21 & 0.05 & (22.69) & 7,476.45 \\\hline \text { Machine } & 5.77 & 0.49 & 11.7 & 0.00 & 4.67 & 6.87 \\\text { Hours } & & & & & \\\hline\end{array}$




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If the controller uses regression analysis to estimate costs, the estimate of the fixed portion of electricity costs is:

A) Fixed Cost = $5.77.
B) Fixed Cost = $1,682.82.
C) Fixed Cost = $1,425.18.
D) Fixed Cost = $3,726.88

Question 76

The College of Business at Northeast College is accumulating data as a first step in the preparation of next year's budget development. One cost that is being looked at closely is administrative costs as a function of student credit hours. Data on administrative costs and credit hours for the past thirteen months are shown below:
$\begin{array}{lrr}& \text { Administrative } & \text { Credit } \\\text { Month } & \text { Costs } & \text { Hours }\\\text { July } & \$ 129,301 & 250 \\\text { August } & 82,613 & 115 \\\text { September } & 225,580 & 1,392 \\\text { October } & 216,394 & 1,000 \\\text { November } & 258,263 & 1,309\\\text { December } & 184,445 & 1,112 \\\text { January } & 219,137 & 1,335 \\\text { February } & 245,000 & 1,373 \\\text { March } & 209,462 & 1,064 \\\text { April } & 191,925 & 1,123 \\\text { May } & 249,978 & 1,360\\\text { June } & 170,41 \varepsilon & 420 \\\text { July } & 128,167 & 315 \\\text { Total } & \$ 2,510,687 & 12,172 \\\text { Average } & \$ 193,130 & 936\end{array}$


The controller's office has analyzed the data and has given you the results from the regression analysis:
$$\begin{array}{c} { \text { SUMMARY OUTPUT } } \\ { \text { Regression Statistics } } \\\begin{array} { | l | r | } \hline \text { Multiple R } & 0.9317157 \\\hline \text { R Square } & 0.868094147 \\\hline \text { Adjusted R Square } & 0.856102705 \\\hline \text { Standard Error } & 20,134.92395 \\\hline \text { Observations } & 13 \\\hline\end{array}\end{array}$$
$$\begin{array} { | l | r | r | r | r | r | } \hline \text { ANOVA } & & & & & \\\hline & \text { df} & \text { S S } & \text { M S } & \text { F } & \text { Significance } \text { F } \\\hline \text { Repression } & 1 & 29,349,143,514 & 29,349,143,514 & 72.3928117 & 3.61909 \mathrm { E } - 06 \\\hline \text { Residual } & 11 & 4,459,566,787 & 405,415,162.4 & & \\\hline \text { Total } & 12 & 33,808,710,301 & & & \\\hline\end{array}$$



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If the controller uses regression analysis to estimate costs, the estimate of the fixed portion of administrative costs is:

A) Fixed Cost = $103.56.
B) Fixed Cost = $12,521.26.
C) Fixed Cost = $19,943.58.
D) Fixed Cost = $96,647.02.

Question 77

Thane Company is interested in establishing the relationship between electricity costs and machine hours. Data have been collected and a regression analysis prepared using Excel. The monthly data and the regression output follow:
$\begin{array}{lll}\text {Month}&\text {Machine Hours}&\text {Electricity Costs}\\\text { January } & 2,500 & \$ 18,400 \\\text { February } & 2,900 & 21,000 \\\text { March } & 1,900& 13,500 \\\text { April } & 3,100 & 23,000 \\\text { May } & 3,800& 28,250 \\\text { June } & 3,300& 22,000 \\\text { July } & 4,100 & 24,750\\\text { August } & 3,500 & 22,750 \\\text { September } & 2,000 & 15,500 \\\text { October } & 3,700 & 26,000 \\\text { November } & 4,700 & 31,000 \\\text { December } & 4,200 & 27,750\end{array}$



$$\begin{array}{c} { \text { Semmary Output } } \\ { \text { Regrencion Statirticr } } \\\begin{array}{ | l | c | } \hline \text { Multiple R } & 0.965 \\\hline \text { R Squuare } & 0.932 \\\hline \text { Adjusted R } ^2 & 0.925 \\\hline \text { Standard Error } & 1,425.18 \\\hline \text { Observations } & 12.00 \\\hline\end{array}\end{array}$$

$\begin{array}{|l|r|r|r|r|r|r|}\hline && \text { Standard } & && \text { Lower } & \text { Upper } \\&\text { Coefficients } & \text { Error } & \text { t Stat } & \text { P-value } & 95 \% & 95 \% \\ \hline \text { Intercept } & 3,726.88 & 1,682.82 & 2.21 & 0.05 & (22.69) & 7,476.45 \\\hline \text { Machine } & 5.77 & 0.49 & 11.7 & 0.00 & 4.67 & 6.87 \\\text { Hours } & & & & & \\\hline\end{array}$



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If the controller uses regression analysis to estimate costs, the cost equation for electricity costs is:

A) Electricity Costs = $1,425.18 + ($12.00 × Machine hours).
B) Electricity Costs = $3,726.88 + ($1,682.82 × Machine hours).
C) Electricity Costs = $1,682.82 + ($0.49 × Machine hours).
D) Electricity Costs = $3,726.88 + ($5.77 × Machine hours).

Question 78

The College of Business at Northeast College is accumulating data as a first step in the preparation of next year's budget development. One cost that is being looked at closely is administrative costs as a function of student credit hours. Data on administrative costs and credit hours for the past thirteen months are shown below:
$\begin{array}{lrr}& \text { Administrative } & \text { Credit } \\\text { Month } & \text { Costs } & \text { Hours }\\\text { July } & \$ 129,301 & 250 \\\text { August } & 82,613 & 115 \\\text { September } & 225,580 & 1,392 \\\text { October } & 216,394 & 1,000 \\\text { November } & 258,263 & 1,309\\\text { December } & 184,445 & 1,112 \\\text { January } & 219,137 & 1,335 \\\text { February } & 245,000 & 1,373 \\\text { March } & 209,462 & 1,064 \\\text { April } & 191,925 & 1,123 \\\text { May } & 249,978 & 1,360\\\text { June } & 170,41 \varepsilon & 420 \\\text { July } & 128,167 & 315 \\\text { Total } & \$ 2,510,687 & 12,172 \\\text { Average } & \$ 193,130 & 936\end{array}$


The controller's office has analyzed the data and has given you the results from the regression analysis:
$$\begin{array}{c} { \text { SUMMARY OUTPUT } } \\ { \text { Regression Statistics } } \\\begin{array} { | l | r | } \hline \text { Multiple R } & 0.9317157 \\\hline \text { R Square } & 0.868094147 \\\hline \text { Adjusted R Square } & 0.856102705 \\\hline \text { Standard Error } & 20,134.92395 \\\hline \text { Observations } & 13 \\\hline\end{array}\end{array}$$
$$\begin{array} { | l | r | r | r | r | r | } \hline \text { ANOVA } & & & & & \\\hline & \text { df} & \text { S S } & \text { M S } & \text { F } & \text { Significance } \text { F } \\\hline \text { Repression } & 1 & 29,349,143,514 & 29,349,143,514 & 72.3928117 & 3.61909 \mathrm { E } - 06 \\\hline \text { Residual } & 11 & 4,459,566,787 & 405,415,162.4 & & \\\hline \text { Total } & 12 & 33,808,710,301 & & & \\\hline\end{array}$$



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The correlation coefficient (rounded to the 3^rd decimal) for the regression equation for administrative costs is:

A) 0.932.
B) 0.868.
C) 0.856.
D) 0.966.

Question 79

Thane Company is interested in establishing the relationship between electricity costs and machine hours. Data have been collected and a regression analysis prepared using Excel. The monthly data and the regression output follow:
$\begin{array}{lll}\text {Month}&\text {Machine Hours}&\text {Electricity Costs}\\\text { January } & 2,500 & \$ 18,400 \\\text { February } & 2,900 & 21,000 \\\text { March } & 1,900& 13,500 \\\text { April } & 3,100 & 23,000 \\\text { May } & 3,800& 28,250 \\\text { June } & 3,300& 22,000 \\\text { July } & 4,100 & 24,750\\\text { August } & 3,500 & 22,750 \\\text { September } & 2,000 & 15,500 \\\text { October } & 3,700 & 26,000 \\\text { November } & 4,700 & 31,000 \\\text { December } & 4,200 & 27,750\end{array}$


$$\begin{array}{c} { \text { Summary Output } } \\ { \text { Regression Statistics } } \\\begin{array}{ | l | c | } \hline \text { Multiple R } & 0.965 \\\hline \text { R Squuare } & 0.932 \\\hline \text { Adjusted R } ^2 & 0.925 \\\hline \text { Standard Error } & 1,425.18 \\\hline \text { Observations } & 12.00 \\\hline\end{array}\end{array}$$


$\begin{array}{|l|r|r|r|r|r|r|}\hline && \text { Standard } & && \text { Lower } & \text { Upper } \\&\text { Coefficients } & \text { Error } & \text { t Stat } & \text { P-value } & 95 \% & 95 \% \\ \hline \text { Intercept } & 3,726.88 & 1,682.82 & 2.21 & 0.05 & (22.69) & 7,476.45 \\\hline \text { Machine } & 5.77 & 0.49 & 11.7 & 0.00 & 4.67 & 6.87 \\\text { Hours } & & & & & \\\hline\end{array}$



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If the controller uses the high-low method to estimate costs, the fixed cost portion of the cost equation for electricity costs is:

A) $3,726.88.
B) $1,425.18.
C) $1,625.00.
D) $22,825.00.

Question 80

Thane Company is interested in establishing the relationship between electricity costs and machine hours. Data have been collected and a regression analysis prepared using Excel. The monthly data and the regression output follow:
$\begin{array}{lll}\text {Month}&\text {Machine Hours}&\text {Electricity Costs}\\\text { January } & 2,500 & \$ 18,400 \\\text { February } & 2,900 & 21,000 \\\text { March } & 1,900& 13,500 \\\text { April } & 3,100 & 23,000 \\\text { May } & 3,800& 28,250 \\\text { June } & 3,300& 22,000 \\\text { July } & 4,100 & 24,750\\\text { August } & 3,500 & 22,750 \\\text { September } & 2,000 & 15,500 \\\text { October } & 3,700 & 26,000 \\\text { November } & 4,700 & 31,000 \\\text { December } & 4,200 & 27,750\end{array}$



$$\begin{array}{c} { \text { Summary Output } } \\ { \text { Regression Statistics } } \\\begin{array}{ | l | c | } \hline \text { Multiple R } & 0.965 \\\hline \text { R Squuare } & 0.932 \\\hline \text { Adjusted R } ^2 & 0.925 \\\hline \text { Standard Error } & 1,425.18 \\\hline \text { Observations } & 12.00 \\\hline\end{array}\end{array}$$


$\begin{array}{|l|r|r|r|r|r|r|}\hline && \text { Standard } & && \text { Lower } & \text { Upper } \\&\text { Coefficients } & \text { Error } & \text { t Stat } & \text { P-value } & 95 \% & 95 \% \\ \hline \text { Intercept } & 3,726.88 & 1,682.82 & 2.21 & 0.05 & (22.69) & 7,476.45 \\\hline \text { Machine } & 5.77 & 0.49 & 11.7 & 0.00 & 4.67 & 6.87 \\\text { Hours } & & & & & \\\hline\end{array}$



-
If the controller uses the high-low method to estimate costs, the cost equation for electricity costs is:

A) Electricity Costs = $3,726.88 + ($5.77 × Machine-hours).
B) Electricity Costs = $1,625.00 + ($6.25 × Machine-hours).
C) Electricity Costs = $6.90 × Machine-hours.
D) Electricity Costs = $22,825.