Question 1

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:

Accepted Answer

The high-low method calculates the variable cost per unit by taking the difference in costs between the highest and lowest activity levels and dividing by the difference in activity levels. Here, the highest cost is $31,000 (4,700 hours) and the lowest is $13,500 (1,900 hours). The variable cost per machine hour is ($31,000 - $13,500) / (4,700 - 1,900) = $6.25.

Question 2

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:

Accepted Answer

The estimate of the variable portion of administrative costs in a month with 200 credit hours is $20,612. This is because the regression equation is Y = 103,196 + 103.06X, where Y is the total administrative costs and X is the number of credit hours. Substituting X = 200 into the equation gives Y = 103,196 + 103.06(200) = $20,612.

Question 3

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:

Accepted Answer

The correct answer is A.The regression equation is:Electricity Costs = 3,726.88 + 5.77 * Machine HoursSubstituting 2,200 machine hours into the equation, we get:Electricity Costs = 3,726.88 + 5.77 * 2,200 = $15,375

Question 4

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:

Accepted Answer

Regression analysis provides an equation that estimates costs based on a fixed component and a variable component. Option C has a reasonable fixed cost and variable cost per credit hour, which aligns with typical regression outputs for cost estimation.

Question 5

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:

Accepted Answer

The answer of The College of Business at Northeast College...

Question 6

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)

Accepted Answer

Management analysis of data is subjective and lacks the quantitative rigor of the other methods, making it less reliable for estimating fixed and variable costs.

Question 7

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:

Accepted Answer

The correlation coefficient is the square root of the R-squared value, which is 0.932.

Question 8

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:

Accepted Answer

The percent of the total variance explained by the regression is given by the R Square value, which is 93.2%.

Question 9

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)

Accepted Answer

The answer of The College of Business at Northeast College...

Question 10

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)

Accepted Answer

The estimate of administrative costs is calculated using the regression equation derived from the analysis, which predicts costs based on credit hours. The closest estimate to the calculated value for 1,000 credit hours is $199,707.

Question 11

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)

Accepted Answer

Multiple regression analysis is used to estimate the relationship between a dependent variable and two or more independent variables, making it suitable for analyzing how inventory warehouse costs are affected by both the number of shipments and the weight of the material handled.

Question 12

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

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

Accepted Answer

The answer of The College of Business at Northeast College...

Question 13

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:

Accepted Answer

The variable portion of electricity costs is represented by the coefficient of the Machine Hours in the regression equation, which is $5.77 per machine hour.

Question 14

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)

Accepted Answer

The answer of Thane Company is interested in establishing the...

Question 15

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

Accepted Answer

The fixed portion of electricity costs is represented by the intercept in the regression output, which is $3,726.88.

Question 16

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 fixed portion of administrative costs is:

Accepted Answer

The answer of The College of Business at Northeast College...

Question 17

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:
$\begin{array}{lll}
\text {Month}&\text {Machine Hours}&\text {Electricity Costs}\
\text { January } & 2,500 & \$ 18,400 \
\text { February } & 2,900 & 21,000 \
\text { March } & 1,900& 13,500 \
\text { April } & 3,100 & 23,000 \
\text { May } & 3,800& 28,250 \
\text { June } & 3,300& 22,000 \
\text { July } & 4,100 & 24,750\
\text { August } & 3,500 & 22,750 \
\text { September } & 2,000 & 15,500 \
\text { October } & 3,700 & 26,000 \
\text { November } & 4,700 & 31,000 \
\text { December } & 4,200 & 27,750
\end{array}$

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

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

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

Accepted Answer

The cost equation is derived from the regression coefficients: the intercept is $3,726.88 and the slope for machine hours is $5.77.

Question 18

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: $$\begin{array}{c} { \text { SUMMARY OUTPUT } } \ { \text { Regression Statistics } } \ \begin{array} { | l | r | } \hline \text { Multiple R } & 0.9317157 \ \hline \text { R Square } & 0.868094147 \ \hline \text { Adjusted R Square } & 0.856102705 \ \hline \text { Standard Error } & 20,134.92395 \ \hline \text { Observations } & 13 \ \hline \end{array}\end{array}$$ $$\begin{array} { | l | r | r | r | r | r | } \hline \text { ANOVA } & & & & & \ \hline & \text { df} & \text { S S } & \text { M S } & \text { F } & \text { Significance } \text { F } \ \hline \text { Repression } & 1 & 29,349,143,514 & 29,349,143,514 & 72.3928117 & 3.61909 \mathrm { E } - 06 \ \hline \text { Residual } & 11 & 4,459,566,787 & 405,415,162.4 & & \ \hline \text { Total } & 12 & 33,808,710,301 & & & \ \hline \end{array}$$ - The correlation coefficient (rounded to the 3^rd decimal) for the regression equation for administrative costs is:

Accepted Answer

The correlation coefficient is a measure of the strength of the linear relationship between two variables. It can range from -1 to 1, where -1 indicates a perfect negative relationship, 0 indicates no relationship, and 1 indicates a perfect positive relationship. In this case, the correlation coefficient is 0.932, which indicates a strong positive relationship between administrative costs and the independent variable(s).

Question 19

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 fixed cost portion of the cost equation for electricity costs is:

Accepted Answer

The high-low method uses the highest and lowest activity levels to estimate fixed and variable costs. The highest machine hours are 4,700 (November) with costs of $31,000, and the lowest are 1,900 (March) with costs of $13,500. The variable cost per machine hour is ($31,000 - $13,500) / (4,700 - 1,900) = $6.25. The fixed cost is calculated as $31,000 - ($6.25 * 4,700) = $1,625.00.

Question 20

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:

Accepted Answer

The answer of Thane Company is interested in establishing the...

Quiz 5: Cost Estimation

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)

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)