5-61
.
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:
Month
Administrative Costs
Credit Hours
July
$129,301
250
August
82,613
115
September
225,580
1,392
October
216,394
1,000
November
258,263
1,309
December
184,449
1,112
January
219,137
1,339
February
245,000
1,373
March
209,462
1,064
April
191,925
1,123
May
249,978
1,360
June
170,418
420
July
128,167
315
Total
$2,510,687
12,172
Average
$193,130
936
The controller’s office has analyzed the data and has given you the results from the regression
analysis:
SUMMARY OUTPUT
Repression Statistics
Multiple R
0.9317157
R Square
0.8680941
47
Adjusted R
Square
0.8561027
05
5-62
Standard
Error
20134.923
95
Observatio
ns
13
ANOVA
df
SS
MS
Significan
ce F
Regression
1
293491435
14
293491435
14
3.61909E-
06
Residual
11
445956678
7
405415162
.4
Total
12
338087103
01
Coefficien
ts
Standard
Error
t Stat
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept
96647.023
98
12641.665
39
7.6451180
3
68822.906
08
124471.14
19
68822.906
08
124471.14
19
X Variable
1
103.06076
97
12.112831
03
8.5083965
41
76.400608
33
129.72093
1
76.400608
33
129.72093
1
65
The College of Business at Northeast College is accumulating data as a first step in the
5-63
.
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:
Month
Administrative Costs
Credit Hours
July
$129,301
250
August
82,613
115
September
225,580
1,392
October
216,394
1,000
November
258,263
1,309
December
184,449
1,112
January
219,137
1,339
February
245,000
1,373
March
209,462
1,064
April
191,925
1,123
May
249,978
1,360
June
170,418
420
July
128,167
315
Total
$2,510,687
12,172
Average
$193,130
936
The controller’s office has analyzed the data and has given you the results from the regression
analysis:
SUMMARY OUTPUT
Repression Statistics
Multiple R
0.9317157
R Square
0.8680941
47
Adjusted R
Square
0.8561027
05
5-64
Standard
Error
20134.923
95
Observatio
ns
13
ANOVA
df
SS
MS
Significan
ce F
Regression
1
293491435
14
293491435
14
3.61909E-
06
Residual
11
445956678
7
405415162
.4
Total
12
338087103
01
Coefficien
ts
Standard
Error
t Stat
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept
96647.023
98
12641.665
39
7.6451180
3
68822.906
08
124471.14
19
68822.906
08
124471.14
19
X Variable
1
103.06076
97
12.112831
03
8.5083965
41
76.400608
33
129.72093
1
76.400608
33
129.72093
1
66
The College of Business at Northeast College is accumulating data as a first step in the
5-65
.
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:
Month
Administrative Costs
Credit Hours
July
$129,301
250
August
82,613
115
September
225,580
1,392
October
216,394
1,000
November
258,263
1,309
December
184,449
1,112
January
219,137
1,339
February
245,000
1,373
March
209,462
1,064
April
191,925
1,123
May
249,978
1,360
June
170,418
420
July
128,167
315
Total
$2,510,687
12,172
Average
$193,130
936
The controller’s office has analyzed the data and has given you the results from the regression
analysis:
SUMMARY OUTPUT
Repression Statistics
Multiple R
0.9317157
R Square
0.8680941
47
Adjusted R
Square
0.8561027
05
5-66
Standard
Error
20134.923
95
Observatio
ns
13
ANOVA
df
SS
MS
Significan
ce F
Regression
1
293491435
14
293491435
14
3.61909E-
06
Residual
11
445956678
7
405415162
.4
Total
12
338087103
01
Coefficien
ts
Standard
Error
t Stat
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept
96647.023
98
12641.665
39
7.6451180
3
68822.906
08
124471.14
19
68822.906
08
124471.14
19
X Variable
1
103.06076
97
12.112831
03
8.5083965
41
76.400608
33
129.72093
1
76.400608
33
129.72093
1
5-67
67
.
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:
Month
Administrative Costs
Credit Hours
July
$129,301
250
August
82,613
115
September
225,580
1,392
October
216,394
1,000
November
258,263
1,309
December
184,449
1,112
January
219,137
1,339
February
245,000
1,373
March
209,462
1,064
April
191,925
1,123
May
249,978
1,360
June
170,418
420
July
128,167
315
Total
$2,510,687
12,172
Average
$193,130
936
The controller’s office has analyzed the data and has given you the results from the regression
analysis:
SUMMARY OUTPUT
Repression Statistics
Multiple R
0.9317157
R Square
0.8680941
47
Adjusted R
Square
0.8561027
05
5-68
Standard
Error
20134.923
95
Observatio
ns
13
ANOVA
df
SS
MS
Significan
ce F
Regression
1
293491435
14
293491435
14
3.61909E-
06
Residual
11
445956678
7
405415162
.4
Total
12
338087103
01
Coefficien
ts
Standard
Error
t Stat
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept
96647.023
98
12641.665
39
7.6451180
3
68822.906
08
124471.14
19
68822.906
08
124471.14
19
X Variable
1
103.06076
97
12.112831
03
8.5083965
41
76.400608
33
129.72093
1
76.400608
33
129.72093
1
68
The College of Business at Northeast College is accumulating data as a first step in the
5-69
.
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:
Month
Administrative Costs
Credit Hours
July
$129,301
250
August
82,613
115
September
225,580
1,392
October
216,394
1,000
November
258,263
1,309
December
184,449
1,112
January
219,137
1,339
February
245,000
1,373
March
209,462
1,064
April
191,925
1,123
May
249,978
1,360
June
170,418
420
July
128,167
315
Total
$2,510,687
12,172
Average
$193,130
936
The controller’s office has analyzed the data and has given you the results from the regression
analysis:
SUMMARY OUTPUT
Repression Statistics
Multiple R
0.9317157
R Square
0.8680941
47
Adjusted R
Square
0.8561027
05
Standard
Error
20134.923
95
Observatio
ns
13
ANOVA
df
SS
MS
Significan
ce F
Regression
1
293491435
14
293491435
14
3.61909E-
06
Residual
11
445956678
7
405415162
.4
Total
12
338087103
01
Coefficien
ts
Standard
Error
t Stat
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept
96647.023
98
12641.665
39
7.6451180
3
68822.906
08
124471.14
19
68822.906
08
124471.14
19
X Variable
1
103.06076
97
12.112831
03
8.5083965
41
76.400608
33
129.72093
1
76.400608
33
129.72093
1
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:
5-71
69.
70.
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)
5-73
71.
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:
Month
Machine Hours
Electricity Costs
January
2,500
18,400
February
2,900
21,000
March
1,900
13,500
April
3,100
23,000
May
3,800
28,250
June
3,300
22,000
July
4,100
24,750
August
3,500
22,750
September
2,000
15,500
October
3,700
26,000
November
4,700
31,000
December
4,200
27,750
Summary Output
Regression Statistics
Multiple R
.965
R Square
.932
Adjusted R2
.925
Standard
Error
1,425.18
Observations
12.00
Coefficients
Standard
Error
t
Stat
P-
value
Lower
95%
Upper
95%
Intercept
3,726.88
1,682.82
2.21
0.05
(22.69)
7,476.45
Machine
Hours
5.77
0.49
11.7
0.00
4.67
6.87
If the controller uses the high-low method to estimate costs, the variable cost per
machine hour is:
5-75
72.
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:
Month
Machine Hours
Electricity Costs
January
2,500
18,400
February
2,900
21,000
March
1,900
13,500
April
3,100
23,000
May
3,800
28,250
June
3,300
22,000
July
4,100
24,750
August
3,500
22,750
September
2,000
15,500
October
3,700
26,000
November
4,700
31,000
December
4,200
27,750
Summary Output
Regression Statistics
Multiple R
.965
R Square
.932
Adjusted R2
.925
Standard
Error
1,425.18
Observations
12.00
Coefficients
Standard
Error
t
Stat
P-
value
Lower
95%
Upper
95%
Intercept
3,726.88
1,682.82
2.21
0.05
(22.69)
7,476.45
Machine
Hours
5.77
0.49
11.7
0.00
4.67
6.87
If the controller uses the high-low method to estimate costs, the fixed cost portion of the
cost equation for electricity cost is:
5-77
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:
Month
Machine Hours
Electricity Costs
January
2,500
18,400
February
2,900
21,000
March
1,900
13,500
April
3,100
23,000
May
3,800
28,250
June
3,300
22,000
July
4,100
24,750
August
3,500
22,750
September
2,000
15,500
October
3,700
26,000
November
4,700
31,000
December
4,200
27,750
Summary Output
Regression Statistics
Multiple R
.965
R Square
.932
Adjusted R2
.925
Standard
Error
1,425.18
Observations
12.00
Coefficients
Standard
Error
t
Stat
P-
value
Lower
95%
Upper
95%
Intercept
3,726.88
1,682.82
2.21
0.05
(22.69)
7,476.45
Machine
Hours
5.77
0.49
11.7
0.00
4.67
6.87
If the controller uses the high-low method to estimate costs, the cost equation for
electricity cost is:
5-79
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:
Month
Machine Hours
Electricity Costs
January
2,500
18,400
February
2,900
21,000
March
1,900
13,500
April
3,100
23,000
May
3,800
28,250
June
3,300
22,000
July
4,100
24,750
August
3,500
22,750
September
2,000
15,500
October
3,700
26,000
November
4,700
31,000
December
4,200
27,750
Summary Output
Regression Statistics
Multiple R
.965
R Square
.932
Adjusted R2
.925
Standard
Error
1,425.18
Observations
12.00
Coefficients
Standard
Error
t
Stat
P-
value
Lower
95%
Upper
95%
Intercept
3,726.88
1,682.82
2.21
0.05
(22.69)
7,476.45
Machine
Hours
5.77
0.49
11.7
0.00
4.67
6.87
Based on the results of the high-low analysis, the estimate of electricity costs in a month
with 2,200 machine hours would be: