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Linear Regression Using Excel® 2010

Managerial Accounting

Prepared by Diane TannerUniversity of North Florida

Chapter 6

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Linear Regression One of several cost estimation methods Used by managers to predict costs at various

activity levels More accurate than other estimation methods

Because it uses all the data points Fits a total cost line through the ‘best-fit’

data points

Goal = create a cost equationTC = FC + VCx

Y = mx + b

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How to Run a Regression in Excel 2010®

Step 1: Acquire cost information for all data pointsStep 2: Be sure the Data Analysis tools are installedStep 3: Click [Data] [Data Analysis] [Regression] Step 4: Select the total cost data for the ‘Y’ range.Step 5: Select the activity data for the ‘X’ range.Step 6: Designate the cell in which you want the

regression to be placed in the output range. Note that Excel® will extend the regression beneath and to the right of the cell you choose.

Excel generates output that uses all the data points.

Regression Using Excel4

Example: Given the cost and sales data for Mix, Inc. use regression analysis in Excel® to determine the regression equation:

Cost Sales

$60,000 $120,000 $65,000 $132,000 $73,000 $168,000

$102,000 $210,000 $108,000 $235,000

Step 1: Type the data into Excel®. Step 2: Assume the Data Analysis ToolPak is

already installed.Step 3: Click [Data] [Data Analysis] [Regression] Step 4: Select the total cost data for the Y range.Step 5: Select the activity data for the X range.

Regression Using Excel5

Cost functiony = 0.44X + 5,841

Step 6: Designate the cell in which you want the regression to be placed in the output range. Press OK.SUMMARY OUTPUT

Regression StatisticsMultiple R 0.983352421R Square 0.966981985

Adjusted R Square 0.955975979Standard Error 4607.904631Observations 5

ANOVA df SS MS F Signific. F

Regression 1 1.87E+09 1.87E+09 87.85949 0.002572Residual 3 63698355 21232785Total 4 1.93E+09

CoefficientsStandard

Error t Stat P-value Lower 95% Upper 95%Lower 95.0%

Upper 95.0%

Intercept 5841.365132 8340.922 0.700326 0.53415 -20703.2 32385.9 -20703.2 32385.9X Variable 1 0.437911184 0.046719 9.373339 0.002572 0.289231 0.586591 0.289231 0.586591

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The End