chapter 05 cost estimation - california state university ... 05 cost estimation 1. the following...
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Chapter 05 Cost Estimation
1 The following manufacturing costs were incurred by the RST Company in 2011
Direct materials $112500 Direct labor 175000 Manufacturing overhead 235000
These costs were incurred to produce 25000 units of product Variable manufacturing overhead was 80 of the direct materials cost In 2012 the direct material and variable overhead costs per unit will increase by 15 but the direct labor costs per unit are not expected to change Fixed manufacturing costs are expected to increase by 75 Required (a) Prepare a cost estimate for an activity level of 20000 units of product in 2012 (b) Determine the total product costs per unit for 2011 and 2012
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Chapter 05 Cost Estimation
1 The following manufacturing costs were incurred by the RST Company in 2011
Direct materials $112500 Direct labor 175000 Manufacturing overhead 235000
These costs were incurred to produce 25000 units of product Variable manufacturing overhead was 80 of the direct materials cost In 2012 the direct material and variable overhead costs per unit will increase by 15 but the direct labor costs per unit are not expected to change Fixed manufacturing costs are expected to increase by 75 Required (a) Prepare a cost estimate for an activity level of20000 units of product in 2012 (b) Determine the total product costs per unit for 2011 and 2012
(a) $482175 (b) 2011 unit cost $52250025000 = $2090 2012 unit cost $48217520000 = $2411
Feedback (a) Variable overhead costs (2011) = 80($112500) = $90000 Fixed overhead costs (2011) = $235000 - $90000 = $145000
2011 2012 Direct materials $112500 $103500 (1) Direct labor 175000 140000 (2) Variable overhead costs 90000 82800 (3)
Fixed overhead costs 145000 ~5lli (4) Total $522500 C482iji0
(1) [($1 12500)25000)(l15)](20000) = $103500 (2) ($175000125000)(20000) = $140000 (3) [($90000)125000)(115)](20000) = $82800 (4) ($145000)(l075) = $155875
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 3 Topic Area Account Analysis Mehod
5-1
2 Haglers Toupees has the following machine hours and production costs for the last six
months of last year ~ ) D r l ose _ ~ l c ~
r rLA-v- Machine Production
Moclh Co~
July $+~ AUGust
L~ September t~1 -ctober~___-~~__---~~
ovember December
If Hagler expects to incur 14000 machine hours in January what will be the estimated total production cost using the high-low method
B $i~ i4~50 gtA~nr -c 00 -D $1088910
231 2 ~
0-10 ~ eX)jV7J
( Jy 6Im)
1 2
2 Haglers Toupees has the following machine hours and production costs for the last six months of last year
1vlachine Production Month Hours Cost July 15000 $12075 August 13500 10800 September 11500 9580 October 15500 12080 November 14800 11692 December 12100 9922
If Hagler expects to incur 14000 machine hours in January what will be the estimated total production cost using the high-low method A $875000 B $1114250 C $2240000 D $1088910
VC per unit = ($12080 - 9580)(15500 - 11500) = $625 FC = $12080 - $625(15500) = $239250 TC = $239250 + $625(14000) = $1114250
AACSB Analytic ACPA FN-Decision Making Blooms Application Difficulty Hard Learning Objective 4 Topic Area High-Low Cost Estimation
5-2
3 The Teal Companys total overhead cost~ various levels of activity are presented bel[j]
(F) eM)Total (v)Direct s~ M~Su ____Month Labor Hours Overhead ~
July $WQQamp ( ~ 7( 2-lOAugust L-vJ ~ 234~000 - ) - ~1igt1 poundgt(70 =shy(q( 3~September 9000 319000 153
October H-t3-~ 340500 - ~ IOSW ) - ~oQ1) ~ Cftr~
(lbo(9gtJ Assume that the overhead costs above consist of utilities supervisory salaries and maintenance The breakdown of these costs at the 9000 direct labor hour level of activity is as follows
Utilities (V) 5 3 $137700 Supervisory Salaries (F) 80000 Maintenance (M) 101300
319 0001
Required (a) Using the high-low method determin( the cost formula for maintenance) (b) Express the companys total overhead costs in linear equation form
37 ~SO I
to 1Sb - amp cnro~ 1
b1- ~ - p -+ 5 0 1 vro
~ -=- l) 0b0
3 The Teal Companys total overhead costs at various levels of activity are presented below
Direct Total Month Labor Hours Overhead July 7500 $272000 August 6000 234000 September 9000 319000 October 10500 340500
Assume that the overhead costs above consist of utilities supervisory salaries and maintenance The breakdown of these costs at the 9000 direct labor hour level of activity is as follows
Utilities (V) $1]7700 Supervisory Salaries (F) 80000 Maintenance (M) 101300
319000
Required (a) Using the high-low method determine the cost formula for maintenance (b) Express the companys total overhead costs in linear equation form
(a) $12000 + 836667 x Direct Labor Hour (b) $92000 + 236667 x Direct Labor Hour
Feedback (A) Utilities per hour = $1377009000 = $1530 per direct labor hour Utility cost at the high point = $1530(10500) = $160650 Utility cost at the low point = $1530(6000) = $91800 Maintenance cost at the high point = $340500 - 80000 - 160650 = $99850 Maintenance cost at the low point = $234000 - 80000 - 91800 = $62200 Maintenance cost per hour = ($99850 - 62200)(10500 - 6000) = $836667 Fixed Maintenance costs per month = $99850 - ($836667) x (10500) = $12000 Total maintenance costs = $12000 + $836667 per Direct Labor Hour (B) Total overhead costs = ($80000 + 12000) + ($1530 + 836667) x Direct Labor Hours = $92000 + $2366667 x Direct Labor Hours
AACSB Analytic AICPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 4 Topic Area High-Low Cost Estimation
5-3
4 A company ran a regression analysis using direct labor hours as the independent variable and manufacturing overhead costs as the dependent variable The results are summarized below
Intercept $14600 Slope $ 1255 Correlation coefficient 931 R-squared 867
The company is planning on operating at a level that would requirOOO direct labor houV per month in the upcoming year Required (a) Use the information from the regression analysis to write the cost estimation equation for the manufacturing overhead costs (b) Compute the estimated manufacturing overhead costs per month for the upcoming year
( fA + bx
(2~(- t 14- bGl) +
~ (DU~5 )~T)LfYMtJH
~ -f () QlSD )CI -SSf )(- 1lttbdO t
~OU Dvtt51wtJ
~ 15 ~7Mo~~ (
4
4 A company ran a regression analysis using direct labor hours as the independent variable and manufacturing overhead costs as the dependent variable The results are summarized below
Intercept $14600 Slope $ 1255 Correlation coefficient 931 R-squared 867
The company is planning on operating at a level that would require 12000 direct labor hours per month in the upcoming year Required (a) Use the information from the regression analysis to write the cost estimation equation for the manufacturing overhead costs (b) Compute the estimated manufacturing overhead costs per month for the upcoming year
(a) Total manufacturing overhead costs = $14600 + ($1255 x Direct Labor Hours) (b) $165200
Feedback (b) Total manufacturing overhead costs = $14600 + ($1255)(12000) = $165200
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Easy Learning Objective 5 Topic Area Obtaining Regression Estimates
5-4
5 The Grind Company has been having some difficulties estimating their manufacturing overhead costs In the past manufacturing overhead costs have been related to production levels However some production managers have indicated that the size of their production lots might also be having an impact on the amount of their monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and average production lot size for 2011
Month Production
(Units) Manufacturing Overhead Cost
Average Monthly Production Lot Size
1 75000 $ 925800 20 2 90000 843875 19 3 65000 910125 24 4 80000 946000 19 5 55000 879000 24 6 50000 825000 18 7 85000 960000 22 8 105000 1053500 25 9 102000 1020000 23 10 68000 905000 20 11 75000 938000 22 12 95000 995000 24
Regression analysis results of the information presented above are as follows Ordinary regression Ctpound ) -----------k~---
Equation ~741 + $30692 x units ( - b Q( 1 t(+ 3 obq2-1v~ r-square ~~vJo-r~ i t ~
Multiple re ression 1 ~ q1 + I 0 1 Equation ~171 + $24918 x units + $1177093 x lot size r-square ~beurottf~
Required (a) Use the results from the ordinary regression and estimate ext months manufacturing overhead costs assuming the company is planning to produc 2 000 units (b) Use the results from the multiple regression an~the next months manufacturing
assuming the company is planning to produ~its with an average lot size of 21 c) Comment on which regression seems to be more appropriate under these circumstances
What additional information would you like to see Be specific
(1 I 471 1+ (z 41 Y )L gt-t1lI1l ) -1-0 17 o 131 x )
G~ r $4lt)bD~ ( vtolJ- shy
5
5 The Grind Company has been having some difficulties estimating their manufacturing overhead costs In the past manufacturing overhead costs have been related to production levels However some production managers have indicated that the size of their production middot lots might also be having an impact on the amount of their monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and average production lot size for 2011
Month Production
(Units) Manufacturing Overhead Cost
Average Monthly Production Lot Size
1 75000 $ 925800 20 2 90000 843875 19 - 65 000 910125 24 4 80000 946000 19 5 55000 879000 24 6 50000 825000 18 7 85000 960000 22 8 105000 1053500 25 9 102000 1020000 23 10 68000 905000 20 11 75000 938000 22 12 95000 995000 24
Regression analysis results of the information presented above are as follows Ordinary regression
Equation $691741 + $30692 x units r-square 628
Multiple regression Equation $482171 + $24918 x units + $11770939 x lot size r-square 777
Required (a) Use the results from the ordinary regression and estimate next months manufacturing overhead costs assuming the company is platming to produce 92000 units (b) Use the results from the multiple regression and estimate the next months manufacturing costs assuming the company is planning to produce 92000 units with an average lot size of 21 (c) Comment on which regression seems to be more appropriate under these circumstances What additional infonnation would you like to see Be specific
5-5
(a) $974107 (b) $958606 (c) The mUltiple regression improves the fit over the ordinary regression The r-square improves from 628 to 777 Additional information may include tests to determine the significance of the coefficients
Feedback (a) Total manufacturing costs = $691741 + ($30692 x 92000) = $974107 (b) Total manufacturing costs = [$482171 + ($24918 x 92000) + ($11770939 x 21)] =
$958606
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-6
6 Cameron Company is interested in establishing the relationship between utility costs and machine hours Data has been collected and a regression analysis prepared using Excel The monthly data and the regression output follow
Month Machine
Hours 3250
Electricity Costs
January 22080 Febmary 3770 25200 March 2470 16200 April 4030 27600 May 4940 33900 June 4290 26400 July 5330 29700 August 4550 27300 September 2600 18600 October 4810 31 200 November 6)10 37200 December 5460 33300
SUlvINIARY OUTPUT Regression Statistics Multiple R 965 R Square 932 Adjusted R-Square 925 Standard Error 171021 Observations 1200
Coefficients Standard
Error t Stat P-vaille Lower
95 Upper
95 Intercept 4~47226 201939 221 0051 -27 23 897174 Machine Hours r 5 329 J- 0455 (1 170 ~ 369E-07 4314 6343
--lt s 2-_0
Required a What is the equation for utility costs using the regression analysis b Does the variable machine hours have statistically significance Explain c Prepare an estimate of utility cos for a month when 3000 machine hours are worked
(c) yenil-Vb +($ S 3vt - VJ 11)
~ M Jf 5j~rh~ 11 70 2 0
1f tf12~ U + (r ~~k X s~) $~~~ 6
(a) $447226 + $5329 x machine hours (b) yes the t-stat of 1l70 exceeds the rough rule of thumb of2 (c) $2045926
Feedback (c) $447226 + $5329 x 3000 = $2045926
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-8
7 The Ottawa Company has traditionally estimated manufacturing overhead costs using production volume Some of the production managers believe that the number of set ups may also have an impact on monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and number of setups for 2011
Month Production
(Units Manufacturing Overhead Cost Number of Setu12s
1 50000 $800100 17 2 65000 752500 16 - ) 40000 795100 21 4 55000 822750 16 5 30000 771225 21 6 25000 706200 15 7 60000 843000 19 8 80000 935200 22 9 77000 901750 20 10 43000 786400 17 11 50000 819600 19 12 70000 880900 21
Regression analysis results of the information presented above are as follows Ordinary regression Equation$650398 + $31 061 x units r-square707 Multiple regression Equation$464481 + $25356 x units + $116316048 x lot size r-square867 Required (a) Use the results from the ordinary regression and estimate next months manufacturing overhead costs assuming the company is planning to produce 75000 units (b) Use the results from the multiple regression and estimate the next months manufacturing costs assuming the company is planning to produce 75000 units with an average lot size of 18 (c) Comment on which regression seems to be more appropriate under these circumstances What additional information would you like to see Be specific
5-9
(a) $883356 (b) $864020 (c) The multiple regression improves the fit over the ordinary regression The r-square improves from 707 to 867 Additional information may include tests to determine the significance of the coefficients
Feedback (a) Total manufacturing costs = $650398 + ($31061 x 75000) = $883356 (b) Total manufacturing costs = [$464481 + ($25356 x 75000) + ($116316048 x 18)] = $864020
AACSBmiddot Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-0
Chapter 05 Cost Estimation
1 The following manufacturing costs were incurred by the RST Company in 2011
Direct materials $112500 Direct labor 175000 Manufacturing overhead 235000
These costs were incurred to produce 25000 units of product Variable manufacturing overhead was 80 of the direct materials cost In 2012 the direct material and variable overhead costs per unit will increase by 15 but the direct labor costs per unit are not expected to change Fixed manufacturing costs are expected to increase by 75 Required (a) Prepare a cost estimate for an activity level of20000 units of product in 2012 (b) Determine the total product costs per unit for 2011 and 2012
(a) $482175 (b) 2011 unit cost $52250025000 = $2090 2012 unit cost $48217520000 = $2411
Feedback (a) Variable overhead costs (2011) = 80($112500) = $90000 Fixed overhead costs (2011) = $235000 - $90000 = $145000
2011 2012 Direct materials $112500 $103500 (1) Direct labor 175000 140000 (2) Variable overhead costs 90000 82800 (3)
Fixed overhead costs 145000 ~5lli (4) Total $522500 C482iji0
(1) [($1 12500)25000)(l15)](20000) = $103500 (2) ($175000125000)(20000) = $140000 (3) [($90000)125000)(115)](20000) = $82800 (4) ($145000)(l075) = $155875
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 3 Topic Area Account Analysis Mehod
5-1
2 Haglers Toupees has the following machine hours and production costs for the last six
months of last year ~ ) D r l ose _ ~ l c ~
r rLA-v- Machine Production
Moclh Co~
July $+~ AUGust
L~ September t~1 -ctober~___-~~__---~~
ovember December
If Hagler expects to incur 14000 machine hours in January what will be the estimated total production cost using the high-low method
B $i~ i4~50 gtA~nr -c 00 -D $1088910
231 2 ~
0-10 ~ eX)jV7J
( Jy 6Im)
1 2
2 Haglers Toupees has the following machine hours and production costs for the last six months of last year
1vlachine Production Month Hours Cost July 15000 $12075 August 13500 10800 September 11500 9580 October 15500 12080 November 14800 11692 December 12100 9922
If Hagler expects to incur 14000 machine hours in January what will be the estimated total production cost using the high-low method A $875000 B $1114250 C $2240000 D $1088910
VC per unit = ($12080 - 9580)(15500 - 11500) = $625 FC = $12080 - $625(15500) = $239250 TC = $239250 + $625(14000) = $1114250
AACSB Analytic ACPA FN-Decision Making Blooms Application Difficulty Hard Learning Objective 4 Topic Area High-Low Cost Estimation
5-2
3 The Teal Companys total overhead cost~ various levels of activity are presented bel[j]
(F) eM)Total (v)Direct s~ M~Su ____Month Labor Hours Overhead ~
July $WQQamp ( ~ 7( 2-lOAugust L-vJ ~ 234~000 - ) - ~1igt1 poundgt(70 =shy(q( 3~September 9000 319000 153
October H-t3-~ 340500 - ~ IOSW ) - ~oQ1) ~ Cftr~
(lbo(9gtJ Assume that the overhead costs above consist of utilities supervisory salaries and maintenance The breakdown of these costs at the 9000 direct labor hour level of activity is as follows
Utilities (V) 5 3 $137700 Supervisory Salaries (F) 80000 Maintenance (M) 101300
319 0001
Required (a) Using the high-low method determin( the cost formula for maintenance) (b) Express the companys total overhead costs in linear equation form
37 ~SO I
to 1Sb - amp cnro~ 1
b1- ~ - p -+ 5 0 1 vro
~ -=- l) 0b0
3 The Teal Companys total overhead costs at various levels of activity are presented below
Direct Total Month Labor Hours Overhead July 7500 $272000 August 6000 234000 September 9000 319000 October 10500 340500
Assume that the overhead costs above consist of utilities supervisory salaries and maintenance The breakdown of these costs at the 9000 direct labor hour level of activity is as follows
Utilities (V) $1]7700 Supervisory Salaries (F) 80000 Maintenance (M) 101300
319000
Required (a) Using the high-low method determine the cost formula for maintenance (b) Express the companys total overhead costs in linear equation form
(a) $12000 + 836667 x Direct Labor Hour (b) $92000 + 236667 x Direct Labor Hour
Feedback (A) Utilities per hour = $1377009000 = $1530 per direct labor hour Utility cost at the high point = $1530(10500) = $160650 Utility cost at the low point = $1530(6000) = $91800 Maintenance cost at the high point = $340500 - 80000 - 160650 = $99850 Maintenance cost at the low point = $234000 - 80000 - 91800 = $62200 Maintenance cost per hour = ($99850 - 62200)(10500 - 6000) = $836667 Fixed Maintenance costs per month = $99850 - ($836667) x (10500) = $12000 Total maintenance costs = $12000 + $836667 per Direct Labor Hour (B) Total overhead costs = ($80000 + 12000) + ($1530 + 836667) x Direct Labor Hours = $92000 + $2366667 x Direct Labor Hours
AACSB Analytic AICPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 4 Topic Area High-Low Cost Estimation
5-3
4 A company ran a regression analysis using direct labor hours as the independent variable and manufacturing overhead costs as the dependent variable The results are summarized below
Intercept $14600 Slope $ 1255 Correlation coefficient 931 R-squared 867
The company is planning on operating at a level that would requirOOO direct labor houV per month in the upcoming year Required (a) Use the information from the regression analysis to write the cost estimation equation for the manufacturing overhead costs (b) Compute the estimated manufacturing overhead costs per month for the upcoming year
( fA + bx
(2~(- t 14- bGl) +
~ (DU~5 )~T)LfYMtJH
~ -f () QlSD )CI -SSf )(- 1lttbdO t
~OU Dvtt51wtJ
~ 15 ~7Mo~~ (
4
4 A company ran a regression analysis using direct labor hours as the independent variable and manufacturing overhead costs as the dependent variable The results are summarized below
Intercept $14600 Slope $ 1255 Correlation coefficient 931 R-squared 867
The company is planning on operating at a level that would require 12000 direct labor hours per month in the upcoming year Required (a) Use the information from the regression analysis to write the cost estimation equation for the manufacturing overhead costs (b) Compute the estimated manufacturing overhead costs per month for the upcoming year
(a) Total manufacturing overhead costs = $14600 + ($1255 x Direct Labor Hours) (b) $165200
Feedback (b) Total manufacturing overhead costs = $14600 + ($1255)(12000) = $165200
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Easy Learning Objective 5 Topic Area Obtaining Regression Estimates
5-4
5 The Grind Company has been having some difficulties estimating their manufacturing overhead costs In the past manufacturing overhead costs have been related to production levels However some production managers have indicated that the size of their production lots might also be having an impact on the amount of their monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and average production lot size for 2011
Month Production
(Units) Manufacturing Overhead Cost
Average Monthly Production Lot Size
1 75000 $ 925800 20 2 90000 843875 19 3 65000 910125 24 4 80000 946000 19 5 55000 879000 24 6 50000 825000 18 7 85000 960000 22 8 105000 1053500 25 9 102000 1020000 23 10 68000 905000 20 11 75000 938000 22 12 95000 995000 24
Regression analysis results of the information presented above are as follows Ordinary regression Ctpound ) -----------k~---
Equation ~741 + $30692 x units ( - b Q( 1 t(+ 3 obq2-1v~ r-square ~~vJo-r~ i t ~
Multiple re ression 1 ~ q1 + I 0 1 Equation ~171 + $24918 x units + $1177093 x lot size r-square ~beurottf~
Required (a) Use the results from the ordinary regression and estimate ext months manufacturing overhead costs assuming the company is planning to produc 2 000 units (b) Use the results from the multiple regression an~the next months manufacturing
assuming the company is planning to produ~its with an average lot size of 21 c) Comment on which regression seems to be more appropriate under these circumstances
What additional information would you like to see Be specific
(1 I 471 1+ (z 41 Y )L gt-t1lI1l ) -1-0 17 o 131 x )
G~ r $4lt)bD~ ( vtolJ- shy
5
5 The Grind Company has been having some difficulties estimating their manufacturing overhead costs In the past manufacturing overhead costs have been related to production levels However some production managers have indicated that the size of their production middot lots might also be having an impact on the amount of their monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and average production lot size for 2011
Month Production
(Units) Manufacturing Overhead Cost
Average Monthly Production Lot Size
1 75000 $ 925800 20 2 90000 843875 19 - 65 000 910125 24 4 80000 946000 19 5 55000 879000 24 6 50000 825000 18 7 85000 960000 22 8 105000 1053500 25 9 102000 1020000 23 10 68000 905000 20 11 75000 938000 22 12 95000 995000 24
Regression analysis results of the information presented above are as follows Ordinary regression
Equation $691741 + $30692 x units r-square 628
Multiple regression Equation $482171 + $24918 x units + $11770939 x lot size r-square 777
Required (a) Use the results from the ordinary regression and estimate next months manufacturing overhead costs assuming the company is platming to produce 92000 units (b) Use the results from the multiple regression and estimate the next months manufacturing costs assuming the company is planning to produce 92000 units with an average lot size of 21 (c) Comment on which regression seems to be more appropriate under these circumstances What additional infonnation would you like to see Be specific
5-5
(a) $974107 (b) $958606 (c) The mUltiple regression improves the fit over the ordinary regression The r-square improves from 628 to 777 Additional information may include tests to determine the significance of the coefficients
Feedback (a) Total manufacturing costs = $691741 + ($30692 x 92000) = $974107 (b) Total manufacturing costs = [$482171 + ($24918 x 92000) + ($11770939 x 21)] =
$958606
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-6
6 Cameron Company is interested in establishing the relationship between utility costs and machine hours Data has been collected and a regression analysis prepared using Excel The monthly data and the regression output follow
Month Machine
Hours 3250
Electricity Costs
January 22080 Febmary 3770 25200 March 2470 16200 April 4030 27600 May 4940 33900 June 4290 26400 July 5330 29700 August 4550 27300 September 2600 18600 October 4810 31 200 November 6)10 37200 December 5460 33300
SUlvINIARY OUTPUT Regression Statistics Multiple R 965 R Square 932 Adjusted R-Square 925 Standard Error 171021 Observations 1200
Coefficients Standard
Error t Stat P-vaille Lower
95 Upper
95 Intercept 4~47226 201939 221 0051 -27 23 897174 Machine Hours r 5 329 J- 0455 (1 170 ~ 369E-07 4314 6343
--lt s 2-_0
Required a What is the equation for utility costs using the regression analysis b Does the variable machine hours have statistically significance Explain c Prepare an estimate of utility cos for a month when 3000 machine hours are worked
(c) yenil-Vb +($ S 3vt - VJ 11)
~ M Jf 5j~rh~ 11 70 2 0
1f tf12~ U + (r ~~k X s~) $~~~ 6
(a) $447226 + $5329 x machine hours (b) yes the t-stat of 1l70 exceeds the rough rule of thumb of2 (c) $2045926
Feedback (c) $447226 + $5329 x 3000 = $2045926
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-8
7 The Ottawa Company has traditionally estimated manufacturing overhead costs using production volume Some of the production managers believe that the number of set ups may also have an impact on monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and number of setups for 2011
Month Production
(Units Manufacturing Overhead Cost Number of Setu12s
1 50000 $800100 17 2 65000 752500 16 - ) 40000 795100 21 4 55000 822750 16 5 30000 771225 21 6 25000 706200 15 7 60000 843000 19 8 80000 935200 22 9 77000 901750 20 10 43000 786400 17 11 50000 819600 19 12 70000 880900 21
Regression analysis results of the information presented above are as follows Ordinary regression Equation$650398 + $31 061 x units r-square707 Multiple regression Equation$464481 + $25356 x units + $116316048 x lot size r-square867 Required (a) Use the results from the ordinary regression and estimate next months manufacturing overhead costs assuming the company is planning to produce 75000 units (b) Use the results from the multiple regression and estimate the next months manufacturing costs assuming the company is planning to produce 75000 units with an average lot size of 18 (c) Comment on which regression seems to be more appropriate under these circumstances What additional information would you like to see Be specific
5-9
(a) $883356 (b) $864020 (c) The multiple regression improves the fit over the ordinary regression The r-square improves from 707 to 867 Additional information may include tests to determine the significance of the coefficients
Feedback (a) Total manufacturing costs = $650398 + ($31061 x 75000) = $883356 (b) Total manufacturing costs = [$464481 + ($25356 x 75000) + ($116316048 x 18)] = $864020
AACSBmiddot Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-0
2 Haglers Toupees has the following machine hours and production costs for the last six
months of last year ~ ) D r l ose _ ~ l c ~
r rLA-v- Machine Production
Moclh Co~
July $+~ AUGust
L~ September t~1 -ctober~___-~~__---~~
ovember December
If Hagler expects to incur 14000 machine hours in January what will be the estimated total production cost using the high-low method
B $i~ i4~50 gtA~nr -c 00 -D $1088910
231 2 ~
0-10 ~ eX)jV7J
( Jy 6Im)
1 2
2 Haglers Toupees has the following machine hours and production costs for the last six months of last year
1vlachine Production Month Hours Cost July 15000 $12075 August 13500 10800 September 11500 9580 October 15500 12080 November 14800 11692 December 12100 9922
If Hagler expects to incur 14000 machine hours in January what will be the estimated total production cost using the high-low method A $875000 B $1114250 C $2240000 D $1088910
VC per unit = ($12080 - 9580)(15500 - 11500) = $625 FC = $12080 - $625(15500) = $239250 TC = $239250 + $625(14000) = $1114250
AACSB Analytic ACPA FN-Decision Making Blooms Application Difficulty Hard Learning Objective 4 Topic Area High-Low Cost Estimation
5-2
3 The Teal Companys total overhead cost~ various levels of activity are presented bel[j]
(F) eM)Total (v)Direct s~ M~Su ____Month Labor Hours Overhead ~
July $WQQamp ( ~ 7( 2-lOAugust L-vJ ~ 234~000 - ) - ~1igt1 poundgt(70 =shy(q( 3~September 9000 319000 153
October H-t3-~ 340500 - ~ IOSW ) - ~oQ1) ~ Cftr~
(lbo(9gtJ Assume that the overhead costs above consist of utilities supervisory salaries and maintenance The breakdown of these costs at the 9000 direct labor hour level of activity is as follows
Utilities (V) 5 3 $137700 Supervisory Salaries (F) 80000 Maintenance (M) 101300
319 0001
Required (a) Using the high-low method determin( the cost formula for maintenance) (b) Express the companys total overhead costs in linear equation form
37 ~SO I
to 1Sb - amp cnro~ 1
b1- ~ - p -+ 5 0 1 vro
~ -=- l) 0b0
3 The Teal Companys total overhead costs at various levels of activity are presented below
Direct Total Month Labor Hours Overhead July 7500 $272000 August 6000 234000 September 9000 319000 October 10500 340500
Assume that the overhead costs above consist of utilities supervisory salaries and maintenance The breakdown of these costs at the 9000 direct labor hour level of activity is as follows
Utilities (V) $1]7700 Supervisory Salaries (F) 80000 Maintenance (M) 101300
319000
Required (a) Using the high-low method determine the cost formula for maintenance (b) Express the companys total overhead costs in linear equation form
(a) $12000 + 836667 x Direct Labor Hour (b) $92000 + 236667 x Direct Labor Hour
Feedback (A) Utilities per hour = $1377009000 = $1530 per direct labor hour Utility cost at the high point = $1530(10500) = $160650 Utility cost at the low point = $1530(6000) = $91800 Maintenance cost at the high point = $340500 - 80000 - 160650 = $99850 Maintenance cost at the low point = $234000 - 80000 - 91800 = $62200 Maintenance cost per hour = ($99850 - 62200)(10500 - 6000) = $836667 Fixed Maintenance costs per month = $99850 - ($836667) x (10500) = $12000 Total maintenance costs = $12000 + $836667 per Direct Labor Hour (B) Total overhead costs = ($80000 + 12000) + ($1530 + 836667) x Direct Labor Hours = $92000 + $2366667 x Direct Labor Hours
AACSB Analytic AICPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 4 Topic Area High-Low Cost Estimation
5-3
4 A company ran a regression analysis using direct labor hours as the independent variable and manufacturing overhead costs as the dependent variable The results are summarized below
Intercept $14600 Slope $ 1255 Correlation coefficient 931 R-squared 867
The company is planning on operating at a level that would requirOOO direct labor houV per month in the upcoming year Required (a) Use the information from the regression analysis to write the cost estimation equation for the manufacturing overhead costs (b) Compute the estimated manufacturing overhead costs per month for the upcoming year
( fA + bx
(2~(- t 14- bGl) +
~ (DU~5 )~T)LfYMtJH
~ -f () QlSD )CI -SSf )(- 1lttbdO t
~OU Dvtt51wtJ
~ 15 ~7Mo~~ (
4
4 A company ran a regression analysis using direct labor hours as the independent variable and manufacturing overhead costs as the dependent variable The results are summarized below
Intercept $14600 Slope $ 1255 Correlation coefficient 931 R-squared 867
The company is planning on operating at a level that would require 12000 direct labor hours per month in the upcoming year Required (a) Use the information from the regression analysis to write the cost estimation equation for the manufacturing overhead costs (b) Compute the estimated manufacturing overhead costs per month for the upcoming year
(a) Total manufacturing overhead costs = $14600 + ($1255 x Direct Labor Hours) (b) $165200
Feedback (b) Total manufacturing overhead costs = $14600 + ($1255)(12000) = $165200
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Easy Learning Objective 5 Topic Area Obtaining Regression Estimates
5-4
5 The Grind Company has been having some difficulties estimating their manufacturing overhead costs In the past manufacturing overhead costs have been related to production levels However some production managers have indicated that the size of their production lots might also be having an impact on the amount of their monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and average production lot size for 2011
Month Production
(Units) Manufacturing Overhead Cost
Average Monthly Production Lot Size
1 75000 $ 925800 20 2 90000 843875 19 3 65000 910125 24 4 80000 946000 19 5 55000 879000 24 6 50000 825000 18 7 85000 960000 22 8 105000 1053500 25 9 102000 1020000 23 10 68000 905000 20 11 75000 938000 22 12 95000 995000 24
Regression analysis results of the information presented above are as follows Ordinary regression Ctpound ) -----------k~---
Equation ~741 + $30692 x units ( - b Q( 1 t(+ 3 obq2-1v~ r-square ~~vJo-r~ i t ~
Multiple re ression 1 ~ q1 + I 0 1 Equation ~171 + $24918 x units + $1177093 x lot size r-square ~beurottf~
Required (a) Use the results from the ordinary regression and estimate ext months manufacturing overhead costs assuming the company is planning to produc 2 000 units (b) Use the results from the multiple regression an~the next months manufacturing
assuming the company is planning to produ~its with an average lot size of 21 c) Comment on which regression seems to be more appropriate under these circumstances
What additional information would you like to see Be specific
(1 I 471 1+ (z 41 Y )L gt-t1lI1l ) -1-0 17 o 131 x )
G~ r $4lt)bD~ ( vtolJ- shy
5
5 The Grind Company has been having some difficulties estimating their manufacturing overhead costs In the past manufacturing overhead costs have been related to production levels However some production managers have indicated that the size of their production middot lots might also be having an impact on the amount of their monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and average production lot size for 2011
Month Production
(Units) Manufacturing Overhead Cost
Average Monthly Production Lot Size
1 75000 $ 925800 20 2 90000 843875 19 - 65 000 910125 24 4 80000 946000 19 5 55000 879000 24 6 50000 825000 18 7 85000 960000 22 8 105000 1053500 25 9 102000 1020000 23 10 68000 905000 20 11 75000 938000 22 12 95000 995000 24
Regression analysis results of the information presented above are as follows Ordinary regression
Equation $691741 + $30692 x units r-square 628
Multiple regression Equation $482171 + $24918 x units + $11770939 x lot size r-square 777
Required (a) Use the results from the ordinary regression and estimate next months manufacturing overhead costs assuming the company is platming to produce 92000 units (b) Use the results from the multiple regression and estimate the next months manufacturing costs assuming the company is planning to produce 92000 units with an average lot size of 21 (c) Comment on which regression seems to be more appropriate under these circumstances What additional infonnation would you like to see Be specific
5-5
(a) $974107 (b) $958606 (c) The mUltiple regression improves the fit over the ordinary regression The r-square improves from 628 to 777 Additional information may include tests to determine the significance of the coefficients
Feedback (a) Total manufacturing costs = $691741 + ($30692 x 92000) = $974107 (b) Total manufacturing costs = [$482171 + ($24918 x 92000) + ($11770939 x 21)] =
$958606
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-6
6 Cameron Company is interested in establishing the relationship between utility costs and machine hours Data has been collected and a regression analysis prepared using Excel The monthly data and the regression output follow
Month Machine
Hours 3250
Electricity Costs
January 22080 Febmary 3770 25200 March 2470 16200 April 4030 27600 May 4940 33900 June 4290 26400 July 5330 29700 August 4550 27300 September 2600 18600 October 4810 31 200 November 6)10 37200 December 5460 33300
SUlvINIARY OUTPUT Regression Statistics Multiple R 965 R Square 932 Adjusted R-Square 925 Standard Error 171021 Observations 1200
Coefficients Standard
Error t Stat P-vaille Lower
95 Upper
95 Intercept 4~47226 201939 221 0051 -27 23 897174 Machine Hours r 5 329 J- 0455 (1 170 ~ 369E-07 4314 6343
--lt s 2-_0
Required a What is the equation for utility costs using the regression analysis b Does the variable machine hours have statistically significance Explain c Prepare an estimate of utility cos for a month when 3000 machine hours are worked
(c) yenil-Vb +($ S 3vt - VJ 11)
~ M Jf 5j~rh~ 11 70 2 0
1f tf12~ U + (r ~~k X s~) $~~~ 6
(a) $447226 + $5329 x machine hours (b) yes the t-stat of 1l70 exceeds the rough rule of thumb of2 (c) $2045926
Feedback (c) $447226 + $5329 x 3000 = $2045926
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-8
7 The Ottawa Company has traditionally estimated manufacturing overhead costs using production volume Some of the production managers believe that the number of set ups may also have an impact on monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and number of setups for 2011
Month Production
(Units Manufacturing Overhead Cost Number of Setu12s
1 50000 $800100 17 2 65000 752500 16 - ) 40000 795100 21 4 55000 822750 16 5 30000 771225 21 6 25000 706200 15 7 60000 843000 19 8 80000 935200 22 9 77000 901750 20 10 43000 786400 17 11 50000 819600 19 12 70000 880900 21
Regression analysis results of the information presented above are as follows Ordinary regression Equation$650398 + $31 061 x units r-square707 Multiple regression Equation$464481 + $25356 x units + $116316048 x lot size r-square867 Required (a) Use the results from the ordinary regression and estimate next months manufacturing overhead costs assuming the company is planning to produce 75000 units (b) Use the results from the multiple regression and estimate the next months manufacturing costs assuming the company is planning to produce 75000 units with an average lot size of 18 (c) Comment on which regression seems to be more appropriate under these circumstances What additional information would you like to see Be specific
5-9
(a) $883356 (b) $864020 (c) The multiple regression improves the fit over the ordinary regression The r-square improves from 707 to 867 Additional information may include tests to determine the significance of the coefficients
Feedback (a) Total manufacturing costs = $650398 + ($31061 x 75000) = $883356 (b) Total manufacturing costs = [$464481 + ($25356 x 75000) + ($116316048 x 18)] = $864020
AACSBmiddot Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-0
2 Haglers Toupees has the following machine hours and production costs for the last six months of last year
1vlachine Production Month Hours Cost July 15000 $12075 August 13500 10800 September 11500 9580 October 15500 12080 November 14800 11692 December 12100 9922
If Hagler expects to incur 14000 machine hours in January what will be the estimated total production cost using the high-low method A $875000 B $1114250 C $2240000 D $1088910
VC per unit = ($12080 - 9580)(15500 - 11500) = $625 FC = $12080 - $625(15500) = $239250 TC = $239250 + $625(14000) = $1114250
AACSB Analytic ACPA FN-Decision Making Blooms Application Difficulty Hard Learning Objective 4 Topic Area High-Low Cost Estimation
5-2
3 The Teal Companys total overhead cost~ various levels of activity are presented bel[j]
(F) eM)Total (v)Direct s~ M~Su ____Month Labor Hours Overhead ~
July $WQQamp ( ~ 7( 2-lOAugust L-vJ ~ 234~000 - ) - ~1igt1 poundgt(70 =shy(q( 3~September 9000 319000 153
October H-t3-~ 340500 - ~ IOSW ) - ~oQ1) ~ Cftr~
(lbo(9gtJ Assume that the overhead costs above consist of utilities supervisory salaries and maintenance The breakdown of these costs at the 9000 direct labor hour level of activity is as follows
Utilities (V) 5 3 $137700 Supervisory Salaries (F) 80000 Maintenance (M) 101300
319 0001
Required (a) Using the high-low method determin( the cost formula for maintenance) (b) Express the companys total overhead costs in linear equation form
37 ~SO I
to 1Sb - amp cnro~ 1
b1- ~ - p -+ 5 0 1 vro
~ -=- l) 0b0
3 The Teal Companys total overhead costs at various levels of activity are presented below
Direct Total Month Labor Hours Overhead July 7500 $272000 August 6000 234000 September 9000 319000 October 10500 340500
Assume that the overhead costs above consist of utilities supervisory salaries and maintenance The breakdown of these costs at the 9000 direct labor hour level of activity is as follows
Utilities (V) $1]7700 Supervisory Salaries (F) 80000 Maintenance (M) 101300
319000
Required (a) Using the high-low method determine the cost formula for maintenance (b) Express the companys total overhead costs in linear equation form
(a) $12000 + 836667 x Direct Labor Hour (b) $92000 + 236667 x Direct Labor Hour
Feedback (A) Utilities per hour = $1377009000 = $1530 per direct labor hour Utility cost at the high point = $1530(10500) = $160650 Utility cost at the low point = $1530(6000) = $91800 Maintenance cost at the high point = $340500 - 80000 - 160650 = $99850 Maintenance cost at the low point = $234000 - 80000 - 91800 = $62200 Maintenance cost per hour = ($99850 - 62200)(10500 - 6000) = $836667 Fixed Maintenance costs per month = $99850 - ($836667) x (10500) = $12000 Total maintenance costs = $12000 + $836667 per Direct Labor Hour (B) Total overhead costs = ($80000 + 12000) + ($1530 + 836667) x Direct Labor Hours = $92000 + $2366667 x Direct Labor Hours
AACSB Analytic AICPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 4 Topic Area High-Low Cost Estimation
5-3
4 A company ran a regression analysis using direct labor hours as the independent variable and manufacturing overhead costs as the dependent variable The results are summarized below
Intercept $14600 Slope $ 1255 Correlation coefficient 931 R-squared 867
The company is planning on operating at a level that would requirOOO direct labor houV per month in the upcoming year Required (a) Use the information from the regression analysis to write the cost estimation equation for the manufacturing overhead costs (b) Compute the estimated manufacturing overhead costs per month for the upcoming year
( fA + bx
(2~(- t 14- bGl) +
~ (DU~5 )~T)LfYMtJH
~ -f () QlSD )CI -SSf )(- 1lttbdO t
~OU Dvtt51wtJ
~ 15 ~7Mo~~ (
4
4 A company ran a regression analysis using direct labor hours as the independent variable and manufacturing overhead costs as the dependent variable The results are summarized below
Intercept $14600 Slope $ 1255 Correlation coefficient 931 R-squared 867
The company is planning on operating at a level that would require 12000 direct labor hours per month in the upcoming year Required (a) Use the information from the regression analysis to write the cost estimation equation for the manufacturing overhead costs (b) Compute the estimated manufacturing overhead costs per month for the upcoming year
(a) Total manufacturing overhead costs = $14600 + ($1255 x Direct Labor Hours) (b) $165200
Feedback (b) Total manufacturing overhead costs = $14600 + ($1255)(12000) = $165200
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Easy Learning Objective 5 Topic Area Obtaining Regression Estimates
5-4
5 The Grind Company has been having some difficulties estimating their manufacturing overhead costs In the past manufacturing overhead costs have been related to production levels However some production managers have indicated that the size of their production lots might also be having an impact on the amount of their monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and average production lot size for 2011
Month Production
(Units) Manufacturing Overhead Cost
Average Monthly Production Lot Size
1 75000 $ 925800 20 2 90000 843875 19 3 65000 910125 24 4 80000 946000 19 5 55000 879000 24 6 50000 825000 18 7 85000 960000 22 8 105000 1053500 25 9 102000 1020000 23 10 68000 905000 20 11 75000 938000 22 12 95000 995000 24
Regression analysis results of the information presented above are as follows Ordinary regression Ctpound ) -----------k~---
Equation ~741 + $30692 x units ( - b Q( 1 t(+ 3 obq2-1v~ r-square ~~vJo-r~ i t ~
Multiple re ression 1 ~ q1 + I 0 1 Equation ~171 + $24918 x units + $1177093 x lot size r-square ~beurottf~
Required (a) Use the results from the ordinary regression and estimate ext months manufacturing overhead costs assuming the company is planning to produc 2 000 units (b) Use the results from the multiple regression an~the next months manufacturing
assuming the company is planning to produ~its with an average lot size of 21 c) Comment on which regression seems to be more appropriate under these circumstances
What additional information would you like to see Be specific
(1 I 471 1+ (z 41 Y )L gt-t1lI1l ) -1-0 17 o 131 x )
G~ r $4lt)bD~ ( vtolJ- shy
5
5 The Grind Company has been having some difficulties estimating their manufacturing overhead costs In the past manufacturing overhead costs have been related to production levels However some production managers have indicated that the size of their production middot lots might also be having an impact on the amount of their monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and average production lot size for 2011
Month Production
(Units) Manufacturing Overhead Cost
Average Monthly Production Lot Size
1 75000 $ 925800 20 2 90000 843875 19 - 65 000 910125 24 4 80000 946000 19 5 55000 879000 24 6 50000 825000 18 7 85000 960000 22 8 105000 1053500 25 9 102000 1020000 23 10 68000 905000 20 11 75000 938000 22 12 95000 995000 24
Regression analysis results of the information presented above are as follows Ordinary regression
Equation $691741 + $30692 x units r-square 628
Multiple regression Equation $482171 + $24918 x units + $11770939 x lot size r-square 777
Required (a) Use the results from the ordinary regression and estimate next months manufacturing overhead costs assuming the company is platming to produce 92000 units (b) Use the results from the multiple regression and estimate the next months manufacturing costs assuming the company is planning to produce 92000 units with an average lot size of 21 (c) Comment on which regression seems to be more appropriate under these circumstances What additional infonnation would you like to see Be specific
5-5
(a) $974107 (b) $958606 (c) The mUltiple regression improves the fit over the ordinary regression The r-square improves from 628 to 777 Additional information may include tests to determine the significance of the coefficients
Feedback (a) Total manufacturing costs = $691741 + ($30692 x 92000) = $974107 (b) Total manufacturing costs = [$482171 + ($24918 x 92000) + ($11770939 x 21)] =
$958606
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-6
6 Cameron Company is interested in establishing the relationship between utility costs and machine hours Data has been collected and a regression analysis prepared using Excel The monthly data and the regression output follow
Month Machine
Hours 3250
Electricity Costs
January 22080 Febmary 3770 25200 March 2470 16200 April 4030 27600 May 4940 33900 June 4290 26400 July 5330 29700 August 4550 27300 September 2600 18600 October 4810 31 200 November 6)10 37200 December 5460 33300
SUlvINIARY OUTPUT Regression Statistics Multiple R 965 R Square 932 Adjusted R-Square 925 Standard Error 171021 Observations 1200
Coefficients Standard
Error t Stat P-vaille Lower
95 Upper
95 Intercept 4~47226 201939 221 0051 -27 23 897174 Machine Hours r 5 329 J- 0455 (1 170 ~ 369E-07 4314 6343
--lt s 2-_0
Required a What is the equation for utility costs using the regression analysis b Does the variable machine hours have statistically significance Explain c Prepare an estimate of utility cos for a month when 3000 machine hours are worked
(c) yenil-Vb +($ S 3vt - VJ 11)
~ M Jf 5j~rh~ 11 70 2 0
1f tf12~ U + (r ~~k X s~) $~~~ 6
(a) $447226 + $5329 x machine hours (b) yes the t-stat of 1l70 exceeds the rough rule of thumb of2 (c) $2045926
Feedback (c) $447226 + $5329 x 3000 = $2045926
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-8
7 The Ottawa Company has traditionally estimated manufacturing overhead costs using production volume Some of the production managers believe that the number of set ups may also have an impact on monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and number of setups for 2011
Month Production
(Units Manufacturing Overhead Cost Number of Setu12s
1 50000 $800100 17 2 65000 752500 16 - ) 40000 795100 21 4 55000 822750 16 5 30000 771225 21 6 25000 706200 15 7 60000 843000 19 8 80000 935200 22 9 77000 901750 20 10 43000 786400 17 11 50000 819600 19 12 70000 880900 21
Regression analysis results of the information presented above are as follows Ordinary regression Equation$650398 + $31 061 x units r-square707 Multiple regression Equation$464481 + $25356 x units + $116316048 x lot size r-square867 Required (a) Use the results from the ordinary regression and estimate next months manufacturing overhead costs assuming the company is planning to produce 75000 units (b) Use the results from the multiple regression and estimate the next months manufacturing costs assuming the company is planning to produce 75000 units with an average lot size of 18 (c) Comment on which regression seems to be more appropriate under these circumstances What additional information would you like to see Be specific
5-9
(a) $883356 (b) $864020 (c) The multiple regression improves the fit over the ordinary regression The r-square improves from 707 to 867 Additional information may include tests to determine the significance of the coefficients
Feedback (a) Total manufacturing costs = $650398 + ($31061 x 75000) = $883356 (b) Total manufacturing costs = [$464481 + ($25356 x 75000) + ($116316048 x 18)] = $864020
AACSBmiddot Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-0
3 The Teal Companys total overhead cost~ various levels of activity are presented bel[j]
(F) eM)Total (v)Direct s~ M~Su ____Month Labor Hours Overhead ~
July $WQQamp ( ~ 7( 2-lOAugust L-vJ ~ 234~000 - ) - ~1igt1 poundgt(70 =shy(q( 3~September 9000 319000 153
October H-t3-~ 340500 - ~ IOSW ) - ~oQ1) ~ Cftr~
(lbo(9gtJ Assume that the overhead costs above consist of utilities supervisory salaries and maintenance The breakdown of these costs at the 9000 direct labor hour level of activity is as follows
Utilities (V) 5 3 $137700 Supervisory Salaries (F) 80000 Maintenance (M) 101300
319 0001
Required (a) Using the high-low method determin( the cost formula for maintenance) (b) Express the companys total overhead costs in linear equation form
37 ~SO I
to 1Sb - amp cnro~ 1
b1- ~ - p -+ 5 0 1 vro
~ -=- l) 0b0
3 The Teal Companys total overhead costs at various levels of activity are presented below
Direct Total Month Labor Hours Overhead July 7500 $272000 August 6000 234000 September 9000 319000 October 10500 340500
Assume that the overhead costs above consist of utilities supervisory salaries and maintenance The breakdown of these costs at the 9000 direct labor hour level of activity is as follows
Utilities (V) $1]7700 Supervisory Salaries (F) 80000 Maintenance (M) 101300
319000
Required (a) Using the high-low method determine the cost formula for maintenance (b) Express the companys total overhead costs in linear equation form
(a) $12000 + 836667 x Direct Labor Hour (b) $92000 + 236667 x Direct Labor Hour
Feedback (A) Utilities per hour = $1377009000 = $1530 per direct labor hour Utility cost at the high point = $1530(10500) = $160650 Utility cost at the low point = $1530(6000) = $91800 Maintenance cost at the high point = $340500 - 80000 - 160650 = $99850 Maintenance cost at the low point = $234000 - 80000 - 91800 = $62200 Maintenance cost per hour = ($99850 - 62200)(10500 - 6000) = $836667 Fixed Maintenance costs per month = $99850 - ($836667) x (10500) = $12000 Total maintenance costs = $12000 + $836667 per Direct Labor Hour (B) Total overhead costs = ($80000 + 12000) + ($1530 + 836667) x Direct Labor Hours = $92000 + $2366667 x Direct Labor Hours
AACSB Analytic AICPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 4 Topic Area High-Low Cost Estimation
5-3
4 A company ran a regression analysis using direct labor hours as the independent variable and manufacturing overhead costs as the dependent variable The results are summarized below
Intercept $14600 Slope $ 1255 Correlation coefficient 931 R-squared 867
The company is planning on operating at a level that would requirOOO direct labor houV per month in the upcoming year Required (a) Use the information from the regression analysis to write the cost estimation equation for the manufacturing overhead costs (b) Compute the estimated manufacturing overhead costs per month for the upcoming year
( fA + bx
(2~(- t 14- bGl) +
~ (DU~5 )~T)LfYMtJH
~ -f () QlSD )CI -SSf )(- 1lttbdO t
~OU Dvtt51wtJ
~ 15 ~7Mo~~ (
4
4 A company ran a regression analysis using direct labor hours as the independent variable and manufacturing overhead costs as the dependent variable The results are summarized below
Intercept $14600 Slope $ 1255 Correlation coefficient 931 R-squared 867
The company is planning on operating at a level that would require 12000 direct labor hours per month in the upcoming year Required (a) Use the information from the regression analysis to write the cost estimation equation for the manufacturing overhead costs (b) Compute the estimated manufacturing overhead costs per month for the upcoming year
(a) Total manufacturing overhead costs = $14600 + ($1255 x Direct Labor Hours) (b) $165200
Feedback (b) Total manufacturing overhead costs = $14600 + ($1255)(12000) = $165200
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Easy Learning Objective 5 Topic Area Obtaining Regression Estimates
5-4
5 The Grind Company has been having some difficulties estimating their manufacturing overhead costs In the past manufacturing overhead costs have been related to production levels However some production managers have indicated that the size of their production lots might also be having an impact on the amount of their monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and average production lot size for 2011
Month Production
(Units) Manufacturing Overhead Cost
Average Monthly Production Lot Size
1 75000 $ 925800 20 2 90000 843875 19 3 65000 910125 24 4 80000 946000 19 5 55000 879000 24 6 50000 825000 18 7 85000 960000 22 8 105000 1053500 25 9 102000 1020000 23 10 68000 905000 20 11 75000 938000 22 12 95000 995000 24
Regression analysis results of the information presented above are as follows Ordinary regression Ctpound ) -----------k~---
Equation ~741 + $30692 x units ( - b Q( 1 t(+ 3 obq2-1v~ r-square ~~vJo-r~ i t ~
Multiple re ression 1 ~ q1 + I 0 1 Equation ~171 + $24918 x units + $1177093 x lot size r-square ~beurottf~
Required (a) Use the results from the ordinary regression and estimate ext months manufacturing overhead costs assuming the company is planning to produc 2 000 units (b) Use the results from the multiple regression an~the next months manufacturing
assuming the company is planning to produ~its with an average lot size of 21 c) Comment on which regression seems to be more appropriate under these circumstances
What additional information would you like to see Be specific
(1 I 471 1+ (z 41 Y )L gt-t1lI1l ) -1-0 17 o 131 x )
G~ r $4lt)bD~ ( vtolJ- shy
5
5 The Grind Company has been having some difficulties estimating their manufacturing overhead costs In the past manufacturing overhead costs have been related to production levels However some production managers have indicated that the size of their production middot lots might also be having an impact on the amount of their monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and average production lot size for 2011
Month Production
(Units) Manufacturing Overhead Cost
Average Monthly Production Lot Size
1 75000 $ 925800 20 2 90000 843875 19 - 65 000 910125 24 4 80000 946000 19 5 55000 879000 24 6 50000 825000 18 7 85000 960000 22 8 105000 1053500 25 9 102000 1020000 23 10 68000 905000 20 11 75000 938000 22 12 95000 995000 24
Regression analysis results of the information presented above are as follows Ordinary regression
Equation $691741 + $30692 x units r-square 628
Multiple regression Equation $482171 + $24918 x units + $11770939 x lot size r-square 777
Required (a) Use the results from the ordinary regression and estimate next months manufacturing overhead costs assuming the company is platming to produce 92000 units (b) Use the results from the multiple regression and estimate the next months manufacturing costs assuming the company is planning to produce 92000 units with an average lot size of 21 (c) Comment on which regression seems to be more appropriate under these circumstances What additional infonnation would you like to see Be specific
5-5
(a) $974107 (b) $958606 (c) The mUltiple regression improves the fit over the ordinary regression The r-square improves from 628 to 777 Additional information may include tests to determine the significance of the coefficients
Feedback (a) Total manufacturing costs = $691741 + ($30692 x 92000) = $974107 (b) Total manufacturing costs = [$482171 + ($24918 x 92000) + ($11770939 x 21)] =
$958606
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-6
6 Cameron Company is interested in establishing the relationship between utility costs and machine hours Data has been collected and a regression analysis prepared using Excel The monthly data and the regression output follow
Month Machine
Hours 3250
Electricity Costs
January 22080 Febmary 3770 25200 March 2470 16200 April 4030 27600 May 4940 33900 June 4290 26400 July 5330 29700 August 4550 27300 September 2600 18600 October 4810 31 200 November 6)10 37200 December 5460 33300
SUlvINIARY OUTPUT Regression Statistics Multiple R 965 R Square 932 Adjusted R-Square 925 Standard Error 171021 Observations 1200
Coefficients Standard
Error t Stat P-vaille Lower
95 Upper
95 Intercept 4~47226 201939 221 0051 -27 23 897174 Machine Hours r 5 329 J- 0455 (1 170 ~ 369E-07 4314 6343
--lt s 2-_0
Required a What is the equation for utility costs using the regression analysis b Does the variable machine hours have statistically significance Explain c Prepare an estimate of utility cos for a month when 3000 machine hours are worked
(c) yenil-Vb +($ S 3vt - VJ 11)
~ M Jf 5j~rh~ 11 70 2 0
1f tf12~ U + (r ~~k X s~) $~~~ 6
(a) $447226 + $5329 x machine hours (b) yes the t-stat of 1l70 exceeds the rough rule of thumb of2 (c) $2045926
Feedback (c) $447226 + $5329 x 3000 = $2045926
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-8
7 The Ottawa Company has traditionally estimated manufacturing overhead costs using production volume Some of the production managers believe that the number of set ups may also have an impact on monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and number of setups for 2011
Month Production
(Units Manufacturing Overhead Cost Number of Setu12s
1 50000 $800100 17 2 65000 752500 16 - ) 40000 795100 21 4 55000 822750 16 5 30000 771225 21 6 25000 706200 15 7 60000 843000 19 8 80000 935200 22 9 77000 901750 20 10 43000 786400 17 11 50000 819600 19 12 70000 880900 21
Regression analysis results of the information presented above are as follows Ordinary regression Equation$650398 + $31 061 x units r-square707 Multiple regression Equation$464481 + $25356 x units + $116316048 x lot size r-square867 Required (a) Use the results from the ordinary regression and estimate next months manufacturing overhead costs assuming the company is planning to produce 75000 units (b) Use the results from the multiple regression and estimate the next months manufacturing costs assuming the company is planning to produce 75000 units with an average lot size of 18 (c) Comment on which regression seems to be more appropriate under these circumstances What additional information would you like to see Be specific
5-9
(a) $883356 (b) $864020 (c) The multiple regression improves the fit over the ordinary regression The r-square improves from 707 to 867 Additional information may include tests to determine the significance of the coefficients
Feedback (a) Total manufacturing costs = $650398 + ($31061 x 75000) = $883356 (b) Total manufacturing costs = [$464481 + ($25356 x 75000) + ($116316048 x 18)] = $864020
AACSBmiddot Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-0
3 The Teal Companys total overhead costs at various levels of activity are presented below
Direct Total Month Labor Hours Overhead July 7500 $272000 August 6000 234000 September 9000 319000 October 10500 340500
Assume that the overhead costs above consist of utilities supervisory salaries and maintenance The breakdown of these costs at the 9000 direct labor hour level of activity is as follows
Utilities (V) $1]7700 Supervisory Salaries (F) 80000 Maintenance (M) 101300
319000
Required (a) Using the high-low method determine the cost formula for maintenance (b) Express the companys total overhead costs in linear equation form
(a) $12000 + 836667 x Direct Labor Hour (b) $92000 + 236667 x Direct Labor Hour
Feedback (A) Utilities per hour = $1377009000 = $1530 per direct labor hour Utility cost at the high point = $1530(10500) = $160650 Utility cost at the low point = $1530(6000) = $91800 Maintenance cost at the high point = $340500 - 80000 - 160650 = $99850 Maintenance cost at the low point = $234000 - 80000 - 91800 = $62200 Maintenance cost per hour = ($99850 - 62200)(10500 - 6000) = $836667 Fixed Maintenance costs per month = $99850 - ($836667) x (10500) = $12000 Total maintenance costs = $12000 + $836667 per Direct Labor Hour (B) Total overhead costs = ($80000 + 12000) + ($1530 + 836667) x Direct Labor Hours = $92000 + $2366667 x Direct Labor Hours
AACSB Analytic AICPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 4 Topic Area High-Low Cost Estimation
5-3
4 A company ran a regression analysis using direct labor hours as the independent variable and manufacturing overhead costs as the dependent variable The results are summarized below
Intercept $14600 Slope $ 1255 Correlation coefficient 931 R-squared 867
The company is planning on operating at a level that would requirOOO direct labor houV per month in the upcoming year Required (a) Use the information from the regression analysis to write the cost estimation equation for the manufacturing overhead costs (b) Compute the estimated manufacturing overhead costs per month for the upcoming year
( fA + bx
(2~(- t 14- bGl) +
~ (DU~5 )~T)LfYMtJH
~ -f () QlSD )CI -SSf )(- 1lttbdO t
~OU Dvtt51wtJ
~ 15 ~7Mo~~ (
4
4 A company ran a regression analysis using direct labor hours as the independent variable and manufacturing overhead costs as the dependent variable The results are summarized below
Intercept $14600 Slope $ 1255 Correlation coefficient 931 R-squared 867
The company is planning on operating at a level that would require 12000 direct labor hours per month in the upcoming year Required (a) Use the information from the regression analysis to write the cost estimation equation for the manufacturing overhead costs (b) Compute the estimated manufacturing overhead costs per month for the upcoming year
(a) Total manufacturing overhead costs = $14600 + ($1255 x Direct Labor Hours) (b) $165200
Feedback (b) Total manufacturing overhead costs = $14600 + ($1255)(12000) = $165200
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Easy Learning Objective 5 Topic Area Obtaining Regression Estimates
5-4
5 The Grind Company has been having some difficulties estimating their manufacturing overhead costs In the past manufacturing overhead costs have been related to production levels However some production managers have indicated that the size of their production lots might also be having an impact on the amount of their monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and average production lot size for 2011
Month Production
(Units) Manufacturing Overhead Cost
Average Monthly Production Lot Size
1 75000 $ 925800 20 2 90000 843875 19 3 65000 910125 24 4 80000 946000 19 5 55000 879000 24 6 50000 825000 18 7 85000 960000 22 8 105000 1053500 25 9 102000 1020000 23 10 68000 905000 20 11 75000 938000 22 12 95000 995000 24
Regression analysis results of the information presented above are as follows Ordinary regression Ctpound ) -----------k~---
Equation ~741 + $30692 x units ( - b Q( 1 t(+ 3 obq2-1v~ r-square ~~vJo-r~ i t ~
Multiple re ression 1 ~ q1 + I 0 1 Equation ~171 + $24918 x units + $1177093 x lot size r-square ~beurottf~
Required (a) Use the results from the ordinary regression and estimate ext months manufacturing overhead costs assuming the company is planning to produc 2 000 units (b) Use the results from the multiple regression an~the next months manufacturing
assuming the company is planning to produ~its with an average lot size of 21 c) Comment on which regression seems to be more appropriate under these circumstances
What additional information would you like to see Be specific
(1 I 471 1+ (z 41 Y )L gt-t1lI1l ) -1-0 17 o 131 x )
G~ r $4lt)bD~ ( vtolJ- shy
5
5 The Grind Company has been having some difficulties estimating their manufacturing overhead costs In the past manufacturing overhead costs have been related to production levels However some production managers have indicated that the size of their production middot lots might also be having an impact on the amount of their monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and average production lot size for 2011
Month Production
(Units) Manufacturing Overhead Cost
Average Monthly Production Lot Size
1 75000 $ 925800 20 2 90000 843875 19 - 65 000 910125 24 4 80000 946000 19 5 55000 879000 24 6 50000 825000 18 7 85000 960000 22 8 105000 1053500 25 9 102000 1020000 23 10 68000 905000 20 11 75000 938000 22 12 95000 995000 24
Regression analysis results of the information presented above are as follows Ordinary regression
Equation $691741 + $30692 x units r-square 628
Multiple regression Equation $482171 + $24918 x units + $11770939 x lot size r-square 777
Required (a) Use the results from the ordinary regression and estimate next months manufacturing overhead costs assuming the company is platming to produce 92000 units (b) Use the results from the multiple regression and estimate the next months manufacturing costs assuming the company is planning to produce 92000 units with an average lot size of 21 (c) Comment on which regression seems to be more appropriate under these circumstances What additional infonnation would you like to see Be specific
5-5
(a) $974107 (b) $958606 (c) The mUltiple regression improves the fit over the ordinary regression The r-square improves from 628 to 777 Additional information may include tests to determine the significance of the coefficients
Feedback (a) Total manufacturing costs = $691741 + ($30692 x 92000) = $974107 (b) Total manufacturing costs = [$482171 + ($24918 x 92000) + ($11770939 x 21)] =
$958606
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-6
6 Cameron Company is interested in establishing the relationship between utility costs and machine hours Data has been collected and a regression analysis prepared using Excel The monthly data and the regression output follow
Month Machine
Hours 3250
Electricity Costs
January 22080 Febmary 3770 25200 March 2470 16200 April 4030 27600 May 4940 33900 June 4290 26400 July 5330 29700 August 4550 27300 September 2600 18600 October 4810 31 200 November 6)10 37200 December 5460 33300
SUlvINIARY OUTPUT Regression Statistics Multiple R 965 R Square 932 Adjusted R-Square 925 Standard Error 171021 Observations 1200
Coefficients Standard
Error t Stat P-vaille Lower
95 Upper
95 Intercept 4~47226 201939 221 0051 -27 23 897174 Machine Hours r 5 329 J- 0455 (1 170 ~ 369E-07 4314 6343
--lt s 2-_0
Required a What is the equation for utility costs using the regression analysis b Does the variable machine hours have statistically significance Explain c Prepare an estimate of utility cos for a month when 3000 machine hours are worked
(c) yenil-Vb +($ S 3vt - VJ 11)
~ M Jf 5j~rh~ 11 70 2 0
1f tf12~ U + (r ~~k X s~) $~~~ 6
(a) $447226 + $5329 x machine hours (b) yes the t-stat of 1l70 exceeds the rough rule of thumb of2 (c) $2045926
Feedback (c) $447226 + $5329 x 3000 = $2045926
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-8
7 The Ottawa Company has traditionally estimated manufacturing overhead costs using production volume Some of the production managers believe that the number of set ups may also have an impact on monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and number of setups for 2011
Month Production
(Units Manufacturing Overhead Cost Number of Setu12s
1 50000 $800100 17 2 65000 752500 16 - ) 40000 795100 21 4 55000 822750 16 5 30000 771225 21 6 25000 706200 15 7 60000 843000 19 8 80000 935200 22 9 77000 901750 20 10 43000 786400 17 11 50000 819600 19 12 70000 880900 21
Regression analysis results of the information presented above are as follows Ordinary regression Equation$650398 + $31 061 x units r-square707 Multiple regression Equation$464481 + $25356 x units + $116316048 x lot size r-square867 Required (a) Use the results from the ordinary regression and estimate next months manufacturing overhead costs assuming the company is planning to produce 75000 units (b) Use the results from the multiple regression and estimate the next months manufacturing costs assuming the company is planning to produce 75000 units with an average lot size of 18 (c) Comment on which regression seems to be more appropriate under these circumstances What additional information would you like to see Be specific
5-9
(a) $883356 (b) $864020 (c) The multiple regression improves the fit over the ordinary regression The r-square improves from 707 to 867 Additional information may include tests to determine the significance of the coefficients
Feedback (a) Total manufacturing costs = $650398 + ($31061 x 75000) = $883356 (b) Total manufacturing costs = [$464481 + ($25356 x 75000) + ($116316048 x 18)] = $864020
AACSBmiddot Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-0
4 A company ran a regression analysis using direct labor hours as the independent variable and manufacturing overhead costs as the dependent variable The results are summarized below
Intercept $14600 Slope $ 1255 Correlation coefficient 931 R-squared 867
The company is planning on operating at a level that would requirOOO direct labor houV per month in the upcoming year Required (a) Use the information from the regression analysis to write the cost estimation equation for the manufacturing overhead costs (b) Compute the estimated manufacturing overhead costs per month for the upcoming year
( fA + bx
(2~(- t 14- bGl) +
~ (DU~5 )~T)LfYMtJH
~ -f () QlSD )CI -SSf )(- 1lttbdO t
~OU Dvtt51wtJ
~ 15 ~7Mo~~ (
4
4 A company ran a regression analysis using direct labor hours as the independent variable and manufacturing overhead costs as the dependent variable The results are summarized below
Intercept $14600 Slope $ 1255 Correlation coefficient 931 R-squared 867
The company is planning on operating at a level that would require 12000 direct labor hours per month in the upcoming year Required (a) Use the information from the regression analysis to write the cost estimation equation for the manufacturing overhead costs (b) Compute the estimated manufacturing overhead costs per month for the upcoming year
(a) Total manufacturing overhead costs = $14600 + ($1255 x Direct Labor Hours) (b) $165200
Feedback (b) Total manufacturing overhead costs = $14600 + ($1255)(12000) = $165200
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Easy Learning Objective 5 Topic Area Obtaining Regression Estimates
5-4
5 The Grind Company has been having some difficulties estimating their manufacturing overhead costs In the past manufacturing overhead costs have been related to production levels However some production managers have indicated that the size of their production lots might also be having an impact on the amount of their monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and average production lot size for 2011
Month Production
(Units) Manufacturing Overhead Cost
Average Monthly Production Lot Size
1 75000 $ 925800 20 2 90000 843875 19 3 65000 910125 24 4 80000 946000 19 5 55000 879000 24 6 50000 825000 18 7 85000 960000 22 8 105000 1053500 25 9 102000 1020000 23 10 68000 905000 20 11 75000 938000 22 12 95000 995000 24
Regression analysis results of the information presented above are as follows Ordinary regression Ctpound ) -----------k~---
Equation ~741 + $30692 x units ( - b Q( 1 t(+ 3 obq2-1v~ r-square ~~vJo-r~ i t ~
Multiple re ression 1 ~ q1 + I 0 1 Equation ~171 + $24918 x units + $1177093 x lot size r-square ~beurottf~
Required (a) Use the results from the ordinary regression and estimate ext months manufacturing overhead costs assuming the company is planning to produc 2 000 units (b) Use the results from the multiple regression an~the next months manufacturing
assuming the company is planning to produ~its with an average lot size of 21 c) Comment on which regression seems to be more appropriate under these circumstances
What additional information would you like to see Be specific
(1 I 471 1+ (z 41 Y )L gt-t1lI1l ) -1-0 17 o 131 x )
G~ r $4lt)bD~ ( vtolJ- shy
5
5 The Grind Company has been having some difficulties estimating their manufacturing overhead costs In the past manufacturing overhead costs have been related to production levels However some production managers have indicated that the size of their production middot lots might also be having an impact on the amount of their monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and average production lot size for 2011
Month Production
(Units) Manufacturing Overhead Cost
Average Monthly Production Lot Size
1 75000 $ 925800 20 2 90000 843875 19 - 65 000 910125 24 4 80000 946000 19 5 55000 879000 24 6 50000 825000 18 7 85000 960000 22 8 105000 1053500 25 9 102000 1020000 23 10 68000 905000 20 11 75000 938000 22 12 95000 995000 24
Regression analysis results of the information presented above are as follows Ordinary regression
Equation $691741 + $30692 x units r-square 628
Multiple regression Equation $482171 + $24918 x units + $11770939 x lot size r-square 777
Required (a) Use the results from the ordinary regression and estimate next months manufacturing overhead costs assuming the company is platming to produce 92000 units (b) Use the results from the multiple regression and estimate the next months manufacturing costs assuming the company is planning to produce 92000 units with an average lot size of 21 (c) Comment on which regression seems to be more appropriate under these circumstances What additional infonnation would you like to see Be specific
5-5
(a) $974107 (b) $958606 (c) The mUltiple regression improves the fit over the ordinary regression The r-square improves from 628 to 777 Additional information may include tests to determine the significance of the coefficients
Feedback (a) Total manufacturing costs = $691741 + ($30692 x 92000) = $974107 (b) Total manufacturing costs = [$482171 + ($24918 x 92000) + ($11770939 x 21)] =
$958606
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-6
6 Cameron Company is interested in establishing the relationship between utility costs and machine hours Data has been collected and a regression analysis prepared using Excel The monthly data and the regression output follow
Month Machine
Hours 3250
Electricity Costs
January 22080 Febmary 3770 25200 March 2470 16200 April 4030 27600 May 4940 33900 June 4290 26400 July 5330 29700 August 4550 27300 September 2600 18600 October 4810 31 200 November 6)10 37200 December 5460 33300
SUlvINIARY OUTPUT Regression Statistics Multiple R 965 R Square 932 Adjusted R-Square 925 Standard Error 171021 Observations 1200
Coefficients Standard
Error t Stat P-vaille Lower
95 Upper
95 Intercept 4~47226 201939 221 0051 -27 23 897174 Machine Hours r 5 329 J- 0455 (1 170 ~ 369E-07 4314 6343
--lt s 2-_0
Required a What is the equation for utility costs using the regression analysis b Does the variable machine hours have statistically significance Explain c Prepare an estimate of utility cos for a month when 3000 machine hours are worked
(c) yenil-Vb +($ S 3vt - VJ 11)
~ M Jf 5j~rh~ 11 70 2 0
1f tf12~ U + (r ~~k X s~) $~~~ 6
(a) $447226 + $5329 x machine hours (b) yes the t-stat of 1l70 exceeds the rough rule of thumb of2 (c) $2045926
Feedback (c) $447226 + $5329 x 3000 = $2045926
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-8
7 The Ottawa Company has traditionally estimated manufacturing overhead costs using production volume Some of the production managers believe that the number of set ups may also have an impact on monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and number of setups for 2011
Month Production
(Units Manufacturing Overhead Cost Number of Setu12s
1 50000 $800100 17 2 65000 752500 16 - ) 40000 795100 21 4 55000 822750 16 5 30000 771225 21 6 25000 706200 15 7 60000 843000 19 8 80000 935200 22 9 77000 901750 20 10 43000 786400 17 11 50000 819600 19 12 70000 880900 21
Regression analysis results of the information presented above are as follows Ordinary regression Equation$650398 + $31 061 x units r-square707 Multiple regression Equation$464481 + $25356 x units + $116316048 x lot size r-square867 Required (a) Use the results from the ordinary regression and estimate next months manufacturing overhead costs assuming the company is planning to produce 75000 units (b) Use the results from the multiple regression and estimate the next months manufacturing costs assuming the company is planning to produce 75000 units with an average lot size of 18 (c) Comment on which regression seems to be more appropriate under these circumstances What additional information would you like to see Be specific
5-9
(a) $883356 (b) $864020 (c) The multiple regression improves the fit over the ordinary regression The r-square improves from 707 to 867 Additional information may include tests to determine the significance of the coefficients
Feedback (a) Total manufacturing costs = $650398 + ($31061 x 75000) = $883356 (b) Total manufacturing costs = [$464481 + ($25356 x 75000) + ($116316048 x 18)] = $864020
AACSBmiddot Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-0
4 A company ran a regression analysis using direct labor hours as the independent variable and manufacturing overhead costs as the dependent variable The results are summarized below
Intercept $14600 Slope $ 1255 Correlation coefficient 931 R-squared 867
The company is planning on operating at a level that would require 12000 direct labor hours per month in the upcoming year Required (a) Use the information from the regression analysis to write the cost estimation equation for the manufacturing overhead costs (b) Compute the estimated manufacturing overhead costs per month for the upcoming year
(a) Total manufacturing overhead costs = $14600 + ($1255 x Direct Labor Hours) (b) $165200
Feedback (b) Total manufacturing overhead costs = $14600 + ($1255)(12000) = $165200
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Easy Learning Objective 5 Topic Area Obtaining Regression Estimates
5-4
5 The Grind Company has been having some difficulties estimating their manufacturing overhead costs In the past manufacturing overhead costs have been related to production levels However some production managers have indicated that the size of their production lots might also be having an impact on the amount of their monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and average production lot size for 2011
Month Production
(Units) Manufacturing Overhead Cost
Average Monthly Production Lot Size
1 75000 $ 925800 20 2 90000 843875 19 3 65000 910125 24 4 80000 946000 19 5 55000 879000 24 6 50000 825000 18 7 85000 960000 22 8 105000 1053500 25 9 102000 1020000 23 10 68000 905000 20 11 75000 938000 22 12 95000 995000 24
Regression analysis results of the information presented above are as follows Ordinary regression Ctpound ) -----------k~---
Equation ~741 + $30692 x units ( - b Q( 1 t(+ 3 obq2-1v~ r-square ~~vJo-r~ i t ~
Multiple re ression 1 ~ q1 + I 0 1 Equation ~171 + $24918 x units + $1177093 x lot size r-square ~beurottf~
Required (a) Use the results from the ordinary regression and estimate ext months manufacturing overhead costs assuming the company is planning to produc 2 000 units (b) Use the results from the multiple regression an~the next months manufacturing
assuming the company is planning to produ~its with an average lot size of 21 c) Comment on which regression seems to be more appropriate under these circumstances
What additional information would you like to see Be specific
(1 I 471 1+ (z 41 Y )L gt-t1lI1l ) -1-0 17 o 131 x )
G~ r $4lt)bD~ ( vtolJ- shy
5
5 The Grind Company has been having some difficulties estimating their manufacturing overhead costs In the past manufacturing overhead costs have been related to production levels However some production managers have indicated that the size of their production middot lots might also be having an impact on the amount of their monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and average production lot size for 2011
Month Production
(Units) Manufacturing Overhead Cost
Average Monthly Production Lot Size
1 75000 $ 925800 20 2 90000 843875 19 - 65 000 910125 24 4 80000 946000 19 5 55000 879000 24 6 50000 825000 18 7 85000 960000 22 8 105000 1053500 25 9 102000 1020000 23 10 68000 905000 20 11 75000 938000 22 12 95000 995000 24
Regression analysis results of the information presented above are as follows Ordinary regression
Equation $691741 + $30692 x units r-square 628
Multiple regression Equation $482171 + $24918 x units + $11770939 x lot size r-square 777
Required (a) Use the results from the ordinary regression and estimate next months manufacturing overhead costs assuming the company is platming to produce 92000 units (b) Use the results from the multiple regression and estimate the next months manufacturing costs assuming the company is planning to produce 92000 units with an average lot size of 21 (c) Comment on which regression seems to be more appropriate under these circumstances What additional infonnation would you like to see Be specific
5-5
(a) $974107 (b) $958606 (c) The mUltiple regression improves the fit over the ordinary regression The r-square improves from 628 to 777 Additional information may include tests to determine the significance of the coefficients
Feedback (a) Total manufacturing costs = $691741 + ($30692 x 92000) = $974107 (b) Total manufacturing costs = [$482171 + ($24918 x 92000) + ($11770939 x 21)] =
$958606
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-6
6 Cameron Company is interested in establishing the relationship between utility costs and machine hours Data has been collected and a regression analysis prepared using Excel The monthly data and the regression output follow
Month Machine
Hours 3250
Electricity Costs
January 22080 Febmary 3770 25200 March 2470 16200 April 4030 27600 May 4940 33900 June 4290 26400 July 5330 29700 August 4550 27300 September 2600 18600 October 4810 31 200 November 6)10 37200 December 5460 33300
SUlvINIARY OUTPUT Regression Statistics Multiple R 965 R Square 932 Adjusted R-Square 925 Standard Error 171021 Observations 1200
Coefficients Standard
Error t Stat P-vaille Lower
95 Upper
95 Intercept 4~47226 201939 221 0051 -27 23 897174 Machine Hours r 5 329 J- 0455 (1 170 ~ 369E-07 4314 6343
--lt s 2-_0
Required a What is the equation for utility costs using the regression analysis b Does the variable machine hours have statistically significance Explain c Prepare an estimate of utility cos for a month when 3000 machine hours are worked
(c) yenil-Vb +($ S 3vt - VJ 11)
~ M Jf 5j~rh~ 11 70 2 0
1f tf12~ U + (r ~~k X s~) $~~~ 6
(a) $447226 + $5329 x machine hours (b) yes the t-stat of 1l70 exceeds the rough rule of thumb of2 (c) $2045926
Feedback (c) $447226 + $5329 x 3000 = $2045926
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-8
7 The Ottawa Company has traditionally estimated manufacturing overhead costs using production volume Some of the production managers believe that the number of set ups may also have an impact on monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and number of setups for 2011
Month Production
(Units Manufacturing Overhead Cost Number of Setu12s
1 50000 $800100 17 2 65000 752500 16 - ) 40000 795100 21 4 55000 822750 16 5 30000 771225 21 6 25000 706200 15 7 60000 843000 19 8 80000 935200 22 9 77000 901750 20 10 43000 786400 17 11 50000 819600 19 12 70000 880900 21
Regression analysis results of the information presented above are as follows Ordinary regression Equation$650398 + $31 061 x units r-square707 Multiple regression Equation$464481 + $25356 x units + $116316048 x lot size r-square867 Required (a) Use the results from the ordinary regression and estimate next months manufacturing overhead costs assuming the company is planning to produce 75000 units (b) Use the results from the multiple regression and estimate the next months manufacturing costs assuming the company is planning to produce 75000 units with an average lot size of 18 (c) Comment on which regression seems to be more appropriate under these circumstances What additional information would you like to see Be specific
5-9
(a) $883356 (b) $864020 (c) The multiple regression improves the fit over the ordinary regression The r-square improves from 707 to 867 Additional information may include tests to determine the significance of the coefficients
Feedback (a) Total manufacturing costs = $650398 + ($31061 x 75000) = $883356 (b) Total manufacturing costs = [$464481 + ($25356 x 75000) + ($116316048 x 18)] = $864020
AACSBmiddot Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-0
5 The Grind Company has been having some difficulties estimating their manufacturing overhead costs In the past manufacturing overhead costs have been related to production levels However some production managers have indicated that the size of their production lots might also be having an impact on the amount of their monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and average production lot size for 2011
Month Production
(Units) Manufacturing Overhead Cost
Average Monthly Production Lot Size
1 75000 $ 925800 20 2 90000 843875 19 3 65000 910125 24 4 80000 946000 19 5 55000 879000 24 6 50000 825000 18 7 85000 960000 22 8 105000 1053500 25 9 102000 1020000 23 10 68000 905000 20 11 75000 938000 22 12 95000 995000 24
Regression analysis results of the information presented above are as follows Ordinary regression Ctpound ) -----------k~---
Equation ~741 + $30692 x units ( - b Q( 1 t(+ 3 obq2-1v~ r-square ~~vJo-r~ i t ~
Multiple re ression 1 ~ q1 + I 0 1 Equation ~171 + $24918 x units + $1177093 x lot size r-square ~beurottf~
Required (a) Use the results from the ordinary regression and estimate ext months manufacturing overhead costs assuming the company is planning to produc 2 000 units (b) Use the results from the multiple regression an~the next months manufacturing
assuming the company is planning to produ~its with an average lot size of 21 c) Comment on which regression seems to be more appropriate under these circumstances
What additional information would you like to see Be specific
(1 I 471 1+ (z 41 Y )L gt-t1lI1l ) -1-0 17 o 131 x )
G~ r $4lt)bD~ ( vtolJ- shy
5
5 The Grind Company has been having some difficulties estimating their manufacturing overhead costs In the past manufacturing overhead costs have been related to production levels However some production managers have indicated that the size of their production middot lots might also be having an impact on the amount of their monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and average production lot size for 2011
Month Production
(Units) Manufacturing Overhead Cost
Average Monthly Production Lot Size
1 75000 $ 925800 20 2 90000 843875 19 - 65 000 910125 24 4 80000 946000 19 5 55000 879000 24 6 50000 825000 18 7 85000 960000 22 8 105000 1053500 25 9 102000 1020000 23 10 68000 905000 20 11 75000 938000 22 12 95000 995000 24
Regression analysis results of the information presented above are as follows Ordinary regression
Equation $691741 + $30692 x units r-square 628
Multiple regression Equation $482171 + $24918 x units + $11770939 x lot size r-square 777
Required (a) Use the results from the ordinary regression and estimate next months manufacturing overhead costs assuming the company is platming to produce 92000 units (b) Use the results from the multiple regression and estimate the next months manufacturing costs assuming the company is planning to produce 92000 units with an average lot size of 21 (c) Comment on which regression seems to be more appropriate under these circumstances What additional infonnation would you like to see Be specific
5-5
(a) $974107 (b) $958606 (c) The mUltiple regression improves the fit over the ordinary regression The r-square improves from 628 to 777 Additional information may include tests to determine the significance of the coefficients
Feedback (a) Total manufacturing costs = $691741 + ($30692 x 92000) = $974107 (b) Total manufacturing costs = [$482171 + ($24918 x 92000) + ($11770939 x 21)] =
$958606
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-6
6 Cameron Company is interested in establishing the relationship between utility costs and machine hours Data has been collected and a regression analysis prepared using Excel The monthly data and the regression output follow
Month Machine
Hours 3250
Electricity Costs
January 22080 Febmary 3770 25200 March 2470 16200 April 4030 27600 May 4940 33900 June 4290 26400 July 5330 29700 August 4550 27300 September 2600 18600 October 4810 31 200 November 6)10 37200 December 5460 33300
SUlvINIARY OUTPUT Regression Statistics Multiple R 965 R Square 932 Adjusted R-Square 925 Standard Error 171021 Observations 1200
Coefficients Standard
Error t Stat P-vaille Lower
95 Upper
95 Intercept 4~47226 201939 221 0051 -27 23 897174 Machine Hours r 5 329 J- 0455 (1 170 ~ 369E-07 4314 6343
--lt s 2-_0
Required a What is the equation for utility costs using the regression analysis b Does the variable machine hours have statistically significance Explain c Prepare an estimate of utility cos for a month when 3000 machine hours are worked
(c) yenil-Vb +($ S 3vt - VJ 11)
~ M Jf 5j~rh~ 11 70 2 0
1f tf12~ U + (r ~~k X s~) $~~~ 6
(a) $447226 + $5329 x machine hours (b) yes the t-stat of 1l70 exceeds the rough rule of thumb of2 (c) $2045926
Feedback (c) $447226 + $5329 x 3000 = $2045926
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-8
7 The Ottawa Company has traditionally estimated manufacturing overhead costs using production volume Some of the production managers believe that the number of set ups may also have an impact on monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and number of setups for 2011
Month Production
(Units Manufacturing Overhead Cost Number of Setu12s
1 50000 $800100 17 2 65000 752500 16 - ) 40000 795100 21 4 55000 822750 16 5 30000 771225 21 6 25000 706200 15 7 60000 843000 19 8 80000 935200 22 9 77000 901750 20 10 43000 786400 17 11 50000 819600 19 12 70000 880900 21
Regression analysis results of the information presented above are as follows Ordinary regression Equation$650398 + $31 061 x units r-square707 Multiple regression Equation$464481 + $25356 x units + $116316048 x lot size r-square867 Required (a) Use the results from the ordinary regression and estimate next months manufacturing overhead costs assuming the company is planning to produce 75000 units (b) Use the results from the multiple regression and estimate the next months manufacturing costs assuming the company is planning to produce 75000 units with an average lot size of 18 (c) Comment on which regression seems to be more appropriate under these circumstances What additional information would you like to see Be specific
5-9
(a) $883356 (b) $864020 (c) The multiple regression improves the fit over the ordinary regression The r-square improves from 707 to 867 Additional information may include tests to determine the significance of the coefficients
Feedback (a) Total manufacturing costs = $650398 + ($31061 x 75000) = $883356 (b) Total manufacturing costs = [$464481 + ($25356 x 75000) + ($116316048 x 18)] = $864020
AACSBmiddot Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-0
5 The Grind Company has been having some difficulties estimating their manufacturing overhead costs In the past manufacturing overhead costs have been related to production levels However some production managers have indicated that the size of their production middot lots might also be having an impact on the amount of their monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and average production lot size for 2011
Month Production
(Units) Manufacturing Overhead Cost
Average Monthly Production Lot Size
1 75000 $ 925800 20 2 90000 843875 19 - 65 000 910125 24 4 80000 946000 19 5 55000 879000 24 6 50000 825000 18 7 85000 960000 22 8 105000 1053500 25 9 102000 1020000 23 10 68000 905000 20 11 75000 938000 22 12 95000 995000 24
Regression analysis results of the information presented above are as follows Ordinary regression
Equation $691741 + $30692 x units r-square 628
Multiple regression Equation $482171 + $24918 x units + $11770939 x lot size r-square 777
Required (a) Use the results from the ordinary regression and estimate next months manufacturing overhead costs assuming the company is platming to produce 92000 units (b) Use the results from the multiple regression and estimate the next months manufacturing costs assuming the company is planning to produce 92000 units with an average lot size of 21 (c) Comment on which regression seems to be more appropriate under these circumstances What additional infonnation would you like to see Be specific
5-5
(a) $974107 (b) $958606 (c) The mUltiple regression improves the fit over the ordinary regression The r-square improves from 628 to 777 Additional information may include tests to determine the significance of the coefficients
Feedback (a) Total manufacturing costs = $691741 + ($30692 x 92000) = $974107 (b) Total manufacturing costs = [$482171 + ($24918 x 92000) + ($11770939 x 21)] =
$958606
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-6
6 Cameron Company is interested in establishing the relationship between utility costs and machine hours Data has been collected and a regression analysis prepared using Excel The monthly data and the regression output follow
Month Machine
Hours 3250
Electricity Costs
January 22080 Febmary 3770 25200 March 2470 16200 April 4030 27600 May 4940 33900 June 4290 26400 July 5330 29700 August 4550 27300 September 2600 18600 October 4810 31 200 November 6)10 37200 December 5460 33300
SUlvINIARY OUTPUT Regression Statistics Multiple R 965 R Square 932 Adjusted R-Square 925 Standard Error 171021 Observations 1200
Coefficients Standard
Error t Stat P-vaille Lower
95 Upper
95 Intercept 4~47226 201939 221 0051 -27 23 897174 Machine Hours r 5 329 J- 0455 (1 170 ~ 369E-07 4314 6343
--lt s 2-_0
Required a What is the equation for utility costs using the regression analysis b Does the variable machine hours have statistically significance Explain c Prepare an estimate of utility cos for a month when 3000 machine hours are worked
(c) yenil-Vb +($ S 3vt - VJ 11)
~ M Jf 5j~rh~ 11 70 2 0
1f tf12~ U + (r ~~k X s~) $~~~ 6
(a) $447226 + $5329 x machine hours (b) yes the t-stat of 1l70 exceeds the rough rule of thumb of2 (c) $2045926
Feedback (c) $447226 + $5329 x 3000 = $2045926
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-8
7 The Ottawa Company has traditionally estimated manufacturing overhead costs using production volume Some of the production managers believe that the number of set ups may also have an impact on monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and number of setups for 2011
Month Production
(Units Manufacturing Overhead Cost Number of Setu12s
1 50000 $800100 17 2 65000 752500 16 - ) 40000 795100 21 4 55000 822750 16 5 30000 771225 21 6 25000 706200 15 7 60000 843000 19 8 80000 935200 22 9 77000 901750 20 10 43000 786400 17 11 50000 819600 19 12 70000 880900 21
Regression analysis results of the information presented above are as follows Ordinary regression Equation$650398 + $31 061 x units r-square707 Multiple regression Equation$464481 + $25356 x units + $116316048 x lot size r-square867 Required (a) Use the results from the ordinary regression and estimate next months manufacturing overhead costs assuming the company is planning to produce 75000 units (b) Use the results from the multiple regression and estimate the next months manufacturing costs assuming the company is planning to produce 75000 units with an average lot size of 18 (c) Comment on which regression seems to be more appropriate under these circumstances What additional information would you like to see Be specific
5-9
(a) $883356 (b) $864020 (c) The multiple regression improves the fit over the ordinary regression The r-square improves from 707 to 867 Additional information may include tests to determine the significance of the coefficients
Feedback (a) Total manufacturing costs = $650398 + ($31061 x 75000) = $883356 (b) Total manufacturing costs = [$464481 + ($25356 x 75000) + ($116316048 x 18)] = $864020
AACSBmiddot Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-0
(a) $974107 (b) $958606 (c) The mUltiple regression improves the fit over the ordinary regression The r-square improves from 628 to 777 Additional information may include tests to determine the significance of the coefficients
Feedback (a) Total manufacturing costs = $691741 + ($30692 x 92000) = $974107 (b) Total manufacturing costs = [$482171 + ($24918 x 92000) + ($11770939 x 21)] =
$958606
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-6
6 Cameron Company is interested in establishing the relationship between utility costs and machine hours Data has been collected and a regression analysis prepared using Excel The monthly data and the regression output follow
Month Machine
Hours 3250
Electricity Costs
January 22080 Febmary 3770 25200 March 2470 16200 April 4030 27600 May 4940 33900 June 4290 26400 July 5330 29700 August 4550 27300 September 2600 18600 October 4810 31 200 November 6)10 37200 December 5460 33300
SUlvINIARY OUTPUT Regression Statistics Multiple R 965 R Square 932 Adjusted R-Square 925 Standard Error 171021 Observations 1200
Coefficients Standard
Error t Stat P-vaille Lower
95 Upper
95 Intercept 4~47226 201939 221 0051 -27 23 897174 Machine Hours r 5 329 J- 0455 (1 170 ~ 369E-07 4314 6343
--lt s 2-_0
Required a What is the equation for utility costs using the regression analysis b Does the variable machine hours have statistically significance Explain c Prepare an estimate of utility cos for a month when 3000 machine hours are worked
(c) yenil-Vb +($ S 3vt - VJ 11)
~ M Jf 5j~rh~ 11 70 2 0
1f tf12~ U + (r ~~k X s~) $~~~ 6
(a) $447226 + $5329 x machine hours (b) yes the t-stat of 1l70 exceeds the rough rule of thumb of2 (c) $2045926
Feedback (c) $447226 + $5329 x 3000 = $2045926
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-8
7 The Ottawa Company has traditionally estimated manufacturing overhead costs using production volume Some of the production managers believe that the number of set ups may also have an impact on monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and number of setups for 2011
Month Production
(Units Manufacturing Overhead Cost Number of Setu12s
1 50000 $800100 17 2 65000 752500 16 - ) 40000 795100 21 4 55000 822750 16 5 30000 771225 21 6 25000 706200 15 7 60000 843000 19 8 80000 935200 22 9 77000 901750 20 10 43000 786400 17 11 50000 819600 19 12 70000 880900 21
Regression analysis results of the information presented above are as follows Ordinary regression Equation$650398 + $31 061 x units r-square707 Multiple regression Equation$464481 + $25356 x units + $116316048 x lot size r-square867 Required (a) Use the results from the ordinary regression and estimate next months manufacturing overhead costs assuming the company is planning to produce 75000 units (b) Use the results from the multiple regression and estimate the next months manufacturing costs assuming the company is planning to produce 75000 units with an average lot size of 18 (c) Comment on which regression seems to be more appropriate under these circumstances What additional information would you like to see Be specific
5-9
(a) $883356 (b) $864020 (c) The multiple regression improves the fit over the ordinary regression The r-square improves from 707 to 867 Additional information may include tests to determine the significance of the coefficients
Feedback (a) Total manufacturing costs = $650398 + ($31061 x 75000) = $883356 (b) Total manufacturing costs = [$464481 + ($25356 x 75000) + ($116316048 x 18)] = $864020
AACSBmiddot Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-0
6 Cameron Company is interested in establishing the relationship between utility costs and machine hours Data has been collected and a regression analysis prepared using Excel The monthly data and the regression output follow
Month Machine
Hours 3250
Electricity Costs
January 22080 Febmary 3770 25200 March 2470 16200 April 4030 27600 May 4940 33900 June 4290 26400 July 5330 29700 August 4550 27300 September 2600 18600 October 4810 31 200 November 6)10 37200 December 5460 33300
SUlvINIARY OUTPUT Regression Statistics Multiple R 965 R Square 932 Adjusted R-Square 925 Standard Error 171021 Observations 1200
Coefficients Standard
Error t Stat P-vaille Lower
95 Upper
95 Intercept 4~47226 201939 221 0051 -27 23 897174 Machine Hours r 5 329 J- 0455 (1 170 ~ 369E-07 4314 6343
--lt s 2-_0
Required a What is the equation for utility costs using the regression analysis b Does the variable machine hours have statistically significance Explain c Prepare an estimate of utility cos for a month when 3000 machine hours are worked
(c) yenil-Vb +($ S 3vt - VJ 11)
~ M Jf 5j~rh~ 11 70 2 0
1f tf12~ U + (r ~~k X s~) $~~~ 6
(a) $447226 + $5329 x machine hours (b) yes the t-stat of 1l70 exceeds the rough rule of thumb of2 (c) $2045926
Feedback (c) $447226 + $5329 x 3000 = $2045926
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-8
7 The Ottawa Company has traditionally estimated manufacturing overhead costs using production volume Some of the production managers believe that the number of set ups may also have an impact on monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and number of setups for 2011
Month Production
(Units Manufacturing Overhead Cost Number of Setu12s
1 50000 $800100 17 2 65000 752500 16 - ) 40000 795100 21 4 55000 822750 16 5 30000 771225 21 6 25000 706200 15 7 60000 843000 19 8 80000 935200 22 9 77000 901750 20 10 43000 786400 17 11 50000 819600 19 12 70000 880900 21
Regression analysis results of the information presented above are as follows Ordinary regression Equation$650398 + $31 061 x units r-square707 Multiple regression Equation$464481 + $25356 x units + $116316048 x lot size r-square867 Required (a) Use the results from the ordinary regression and estimate next months manufacturing overhead costs assuming the company is planning to produce 75000 units (b) Use the results from the multiple regression and estimate the next months manufacturing costs assuming the company is planning to produce 75000 units with an average lot size of 18 (c) Comment on which regression seems to be more appropriate under these circumstances What additional information would you like to see Be specific
5-9
(a) $883356 (b) $864020 (c) The multiple regression improves the fit over the ordinary regression The r-square improves from 707 to 867 Additional information may include tests to determine the significance of the coefficients
Feedback (a) Total manufacturing costs = $650398 + ($31061 x 75000) = $883356 (b) Total manufacturing costs = [$464481 + ($25356 x 75000) + ($116316048 x 18)] = $864020
AACSBmiddot Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-0
(a) $447226 + $5329 x machine hours (b) yes the t-stat of 1l70 exceeds the rough rule of thumb of2 (c) $2045926
Feedback (c) $447226 + $5329 x 3000 = $2045926
AACSB Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-8
7 The Ottawa Company has traditionally estimated manufacturing overhead costs using production volume Some of the production managers believe that the number of set ups may also have an impact on monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and number of setups for 2011
Month Production
(Units Manufacturing Overhead Cost Number of Setu12s
1 50000 $800100 17 2 65000 752500 16 - ) 40000 795100 21 4 55000 822750 16 5 30000 771225 21 6 25000 706200 15 7 60000 843000 19 8 80000 935200 22 9 77000 901750 20 10 43000 786400 17 11 50000 819600 19 12 70000 880900 21
Regression analysis results of the information presented above are as follows Ordinary regression Equation$650398 + $31 061 x units r-square707 Multiple regression Equation$464481 + $25356 x units + $116316048 x lot size r-square867 Required (a) Use the results from the ordinary regression and estimate next months manufacturing overhead costs assuming the company is planning to produce 75000 units (b) Use the results from the multiple regression and estimate the next months manufacturing costs assuming the company is planning to produce 75000 units with an average lot size of 18 (c) Comment on which regression seems to be more appropriate under these circumstances What additional information would you like to see Be specific
5-9
(a) $883356 (b) $864020 (c) The multiple regression improves the fit over the ordinary regression The r-square improves from 707 to 867 Additional information may include tests to determine the significance of the coefficients
Feedback (a) Total manufacturing costs = $650398 + ($31061 x 75000) = $883356 (b) Total manufacturing costs = [$464481 + ($25356 x 75000) + ($116316048 x 18)] = $864020
AACSBmiddot Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-0
7 The Ottawa Company has traditionally estimated manufacturing overhead costs using production volume Some of the production managers believe that the number of set ups may also have an impact on monthly manufacturing overhead costs In order to investigate this possibility the company collected information on their monthly manufacturing overhead costs production in units and number of setups for 2011
Month Production
(Units Manufacturing Overhead Cost Number of Setu12s
1 50000 $800100 17 2 65000 752500 16 - ) 40000 795100 21 4 55000 822750 16 5 30000 771225 21 6 25000 706200 15 7 60000 843000 19 8 80000 935200 22 9 77000 901750 20 10 43000 786400 17 11 50000 819600 19 12 70000 880900 21
Regression analysis results of the information presented above are as follows Ordinary regression Equation$650398 + $31 061 x units r-square707 Multiple regression Equation$464481 + $25356 x units + $116316048 x lot size r-square867 Required (a) Use the results from the ordinary regression and estimate next months manufacturing overhead costs assuming the company is planning to produce 75000 units (b) Use the results from the multiple regression and estimate the next months manufacturing costs assuming the company is planning to produce 75000 units with an average lot size of 18 (c) Comment on which regression seems to be more appropriate under these circumstances What additional information would you like to see Be specific
5-9
(a) $883356 (b) $864020 (c) The multiple regression improves the fit over the ordinary regression The r-square improves from 707 to 867 Additional information may include tests to determine the significance of the coefficients
Feedback (a) Total manufacturing costs = $650398 + ($31061 x 75000) = $883356 (b) Total manufacturing costs = [$464481 + ($25356 x 75000) + ($116316048 x 18)] = $864020
AACSBmiddot Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-0
(a) $883356 (b) $864020 (c) The multiple regression improves the fit over the ordinary regression The r-square improves from 707 to 867 Additional information may include tests to determine the significance of the coefficients
Feedback (a) Total manufacturing costs = $650398 + ($31061 x 75000) = $883356 (b) Total manufacturing costs = [$464481 + ($25356 x 75000) + ($116316048 x 18)] = $864020
AACSBmiddot Analytic ACPA FN-Decision Making Blooms Analysis Difficulty Medium Learning Objective 5 Topic Area Obtaining Regression Estimates
5-0