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72. Georgia Meadows Company uses the high-low method to analyze production costs. The
following information relates to the production data for the first six months of the year.
Month Cost(Y) Hours(H)
January $8,542 6,530
February $7,750 5,950
March $9,700 7,500
April $7,435 5,700
May $7,200 5,500
June $9,263 6,750
What is the estimated total cost at an operating level of 8,000 hours?
73. Condor Airplane Company has built a new model jet aircraft which it intends to sell to
high net worth clients. This aircraft required 25,000 hours to complete. Condor believes an
incremental unit-time learning model with an 82% learning curve best reflects the company's
production efficiency. Condor just received a contract to make fifteen identical aircraft. What will
be the expected unit time for the sixteenth aircraft?
74. Which of the following is not one of the main issues regarding data collection which can
significantly affect precision and reliability when using regression or any other cost estimation
method?
75. The R-squared in a satisfactory regression should be:
76. The standard error of the estimate (SE) in a regression analysis is:
77. Which of the following is not usually influenced by learning curve analysis?
78. A range around the regression line within which the management accountant can rely
that the actual value of the predicted cost will fall is referred to as:
79. Cost estimation includes all of the following steps except:
80. Place the six cost estimation steps into the correct order:
1. Determine the cost drivers
2. Graph the data
3. Select and employ the appropriate estimation method
4. Define the cost object for which the related costs are to be estimated
5. Evaluate the accuracy of the cost estimate
6. Collect consistent and accurate data on the cost object and the cost drivers
81. Burmer Co. has accumulated data to use in preparing its annual profit plan for the
upcoming year. The cost behavior pattern of the maintenance costs must be determined. Data
regarding the machine hours and maintenance costs for the last year and the results of the
regression analysis are as follows:
Month Maintenance Cost Machine Hours
Jan. $5,040 620
Feb. 3,600 420
Mar. 4,320 520
Apr. 3,380 390
May 5,220 650
June 3,550 400
July 3,640 430
Aug. 5,360 680
Sept. 5,110 640
Oct. 4,860 610
Nov. 3,960 460
Dec. 3,790 440
Sum $51,830 6,260
Average $4,319 522
A staff assistant has run regression analyses on the data and obtained the following output
using Excel:
REGRESSION ANALYSIS
Y (Dependent) Variable: Maintenance Cost
X (Independent) Variable: Maintenance Hours
Regression Statistics
Multiple R 0.998210294
R Square 0.996423791
Adjusted R Square 0.99606617
Standard Error 47.0629563
Observations 12
ANOVA
df
SS
MS
F
Significance F
Regression 1 6171342.448 6171342 2786.257 1.44166E-13
Residual 10 22149.21856 2214.922
Total 11 6193491.667
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept 783.7782188 68.34114772 11.46861 4.47E-07 631.504653 936.051785
Hours 6.777102456 0.12839066 52.78501 1.44E-13 6.491030239 7.06317467
The t statistic for the independent variable:
82. Burmer Co. has accumulated data to use in preparing its annual profit plan for the
upcoming year. The cost behavior pattern of the maintenance costs must be determined. Data
regarding the machine hours and maintenance costs for the last year and the results of the
regression analysis are as follows:
Month Maintenance Cost Machine Hours
Jan. $5,040 620
Feb. 3,600 420
Mar. 4,320 520
Apr. 3,380 390
May 5,220 650
June 3,550 400
July 3,640 430
Aug. 5,360 680
Sept. 5,110 640
Oct. 4,860 610
Nov. 3,960 460
Dec. 3,790 440
Sum $51,830 6,260
Average $4,319 522
A staff assistant has run regression analyses on the data and obtained the following output
using Excel:
REGRESSION ANALYSIS
Y (Dependent) Variable: Maintenance Cost
X (Independent) Variable: Maintenance Hours
Regression Statistics
Multiple R 0.998210294
R Square 0.996423791
Adjusted R Square 0.99606617
Standard Error 47.0629563
Observations 12
ANOVA
df
SS
MS
F
Significance F
Regression 1 6171342.448 6171342 2786.257 1.44166E-13
Residual 10 22149.21856 2214.922
Total 11 6193491.667
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept 783.7782188 68.34114772 11.46861 4.47E-07 631.504653 936.051785
Hours 6.777102456 0.12839066 52.78501 1.44E-13 6.491030239 7.06317467
The 95% confidence range for a prediction of monthly manufacturing cost using the model is:
83. Burmer Co. has accumulated data to use in preparing its annual profit plan for the
upcoming year. The cost behavior pattern of the maintenance costs must be determined. Data
regarding the machine hours and maintenance costs for the last year and the results of the
regression analysis are as follows:
Month Maintenance Cost Machine Hours
Jan. $5,040 620
Feb. 3,600 420
Mar. 4,320 520
Apr. 3,380 390
May 5,220 650
June 3,550 400
July 3,640 430
Aug. 5,360 680
Sept. 5,110 640
Oct. 4,860 610
Nov. 3,960 460
Dec. 3,790 440
Sum $51,830 6,260
Average $4,319 522
A staff assistant has run regression analyses on the data and obtained the following output
using Excel:
REGRESSION ANALYSIS
Y (Dependent) Variable: Maintenance Cost
X (Independent) Variable: Maintenance Hours
Regression Statistics
Multiple R 0.998210294
R Square 0.996423791
Adjusted R Square 0.99606617
Standard Error 47.0629563
Observations 12
ANOVA
df
SS
MS
F
Significance F
Regression 1 6171342.448 6171342 2786.257 1.44166E-13
Residual 10 22149.21856 2214.922
Total 11 6193491.667
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept 783.7782188 68.34114772 11.46861 4.47E-07 631.504653 936.051785
Hours 6.777102456 0.12839066 52.78501 1.44E-13 6.491030239 7.06317467
The p-value is a measure of:
84. Burmer Co. has accumulated data to use in preparing its annual profit plan for the
upcoming year. The cost behavior pattern of the maintenance costs must be determined. Data
regarding the machine hours and maintenance costs for the last year and the results of the
regression analysis are as follows:
Month Maintenance Cost Machine Hours
Jan. $5,040 620
Feb. 3,600 420
Mar. 4,320 520
Apr. 3,380 390
May 5,220 650
June 3,550 400
July 3,640 430
Aug. 5,360 680
Sept. 5,110 640
Oct. 4,860 610
Nov. 3,960 460
Dec. 3,790 440
Sum $51,830 6,260
Average $4,319 522
A staff assistant has run regression analyses on the data and obtained the following output
using Excel:
REGRESSION ANALYSIS
Y (Dependent) Variable: Maintenance Cost
X (Independent) Variable: Maintenance Hours
Regression Statistics
Multiple R 0.998210294
R Square 0.996423791
Adjusted R Square 0.99606617
Standard Error 47.0629563
Observations 12
ANOVA
df
SS
MS
F
Significance F
Regression 1 6171342.448 6171342 2786.257 1.44166E-13
Residual 10 22149.21856 2214.922
Total 11 6193491.667
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept 783.7782188 68.34114772 11.46861 4.47E-07 631.504653 936.051785
Hours 6.777102456 0.12839066 52.78501 1.44E-13 6.491030239 7.06317467
The statistic that indicates precision of the regression is:
85. Burmer Co. has accumulated data to use in preparing its annual profit plan for the
upcoming year. The cost behavior pattern of the maintenance costs must be determined. Data
regarding the machine hours and maintenance costs for the last year and the results of the
regression analysis are as follows:
Month Maintenance Cost Machine Hours
Jan. $5,040 620
Feb. 3,600 420
Mar. 4,320 520
Apr. 3,380 390
May 5,220 650
June 3,550 400
July 3,640 430
Aug. 5,360 680
Sept. 5,110 640
Oct. 4,860 610
Nov. 3,960 460
Dec. 3,790 440
Sum $51,830 6,260
Average $4,319 522
A staff assistant has run regression analyses on the data and obtained the following output
using Excel:
REGRESSION ANALYSIS
Y (Dependent) Variable: Maintenance Cost
X (Independent) Variable: Maintenance Hours
Regression Statistics
Multiple R 0.998210294
R Square 0.996423791
Adjusted R Square 0.99606617
Standard Error 47.0629563
Observations 12
ANOVA
df
SS
MS
F
Significance F
Regression 1 6171342.448 6171342 2786.257 1.44166E-13
Residual 10 22149.21856 2214.922
Total 11 6193491.667
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept 783.7782188 68.34114772 11.46861 4.47E-07 631.504653 936.051785
Hours 6.777102456 0.12839066 52.78501 1.44E-13 6.491030239 7.06317467
A key statistic that indicates reliability of the regression is:
86. Burmer Co. has accumulated data to use in preparing its annual profit plan for the
upcoming year. The cost behavior pattern of the maintenance costs must be determined. Data
regarding the machine hours and maintenance costs for the last year and the results of the
regression analysis are as follows:
Month Maintenance Cost Machine Hours
Jan. $5,040 620
Feb. 3,600 420
Mar. 4,320 520
Apr. 3,380 390
May 5,220 650
June 3,550 400
July 3,640 430
Aug. 5,360 680
Sept. 5,110 640
Oct. 4,860 610
Nov. 3,960 460
Dec. 3,790 440
Sum $51,830 6,260
Average $4,319 522
A staff assistant has run regression analyses on the data and obtained the following output
using Excel:
REGRESSION ANALYSIS
Y (Dependent) Variable: Maintenance Cost
X (Independent) Variable: Maintenance Hours
Regression Statistics
Multiple R 0.998210294
R Square 0.996423791
Adjusted R Square 0.99606617
Standard Error 47.0629563
Observations 12
ANOVA
df
SS
MS
F
Significance F
Regression 1 6171342.448 6171342 2786.257 1.44166E-13
Residual 10 22149.21856 2214.922
Total 11 6193491.667
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept 783.7782188 68.34114772 11.46861 4.47E-07 631.504653 936.051785
Hours 6.777102456 0.12839066 52.78501 1.44E-13 6.491030239 7.06317467
The
Lower
95%
and
Upper
95%
shown in the output suggests that:
87. Burmer Co. has accumulated data to use in preparing its annual profit plan for the
upcoming year. The cost behavior pattern of the maintenance costs must be determined. Data
regarding the machine hours and maintenance costs for the last year and the results of the
regression analysis are as follows:
Month Maintenance Cost Machine Hours
Jan. $5,040 620
Feb. 3,600 420
Mar. 4,320 520
Apr. 3,380 390
May 5,220 650
June 3,550 400
July 3,640 430
Aug. 5,360 680
Sept. 5,110 640
Oct. 4,860 610
Nov. 3,960 460
Dec. 3,790 440
Sum $51,830 6,260
Average $4,319 522
A staff assistant has run regression analyses on the data and obtained the following output
using Excel:
REGRESSION ANALYSIS
Y (Dependent) Variable: Maintenance Cost
X (Independent) Variable: Maintenance Hours
Regression Statistics
Multiple R 0.998210294
R Square 0.996423791
Adjusted R Square 0.99606617
Standard Error 47.0629563
Observations 12
ANOVA
df
SS
MS
F
Significance F
Regression 1 6171342.448 6171342 2786.257 1.44166E-13
Residual 10 22149.21856 2214.922
Total 11 6193491.667
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept 783.7782188 68.34114772 11.46861 4.47E-07 631.504653 936.051785
Hours 6.777102456 0.12839066 52.78501 1.44E-13 6.491030239 7.06317467
Using regression analysis, what is the estimated maintenance expense for a month that the firm
expects to operate 600 machine hours (round to nearest whole dollar)?
88. A measure of the statistical reliability of each independent variable is:
