Download presentation

Presentation is loading. Please wait.

Published byBrice Singleton Modified over 9 years ago

1
Linear Regression Using Excel 2010 Linear Regression Using Excel ® 2010 Managerial Accounting Prepared by Diane Tanner University of North Florida Chapter 6

2
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 equation TC = FC + VCx Y = mx + b Goal = create a cost equation TC = FC + VCx Y = mx + b 2

3
How to Run a Regression in Excel 2010 ® Step 1: Acquire cost information for all data points Step 2: Be sure the Data Analysis tools are 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. 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. 3 Excel generates output that uses all the data points.

4
Regression Using Excel 4 Example: Given the cost and sales data for Mix, Inc. use regression analysis in Excel ® to determine the regression equation: 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.

5
Regression Using Excel 5 Cost function y = 0.44X + 5,841 Cost function y = 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 Statistics Multiple R0.983352421 R Square0.966981985 Adjusted R Square0.955975979 Standard Error4607.904631 Observations5 ANOVA dfSSMSFSignific. F Regression11.87E+09 87.859490.002572 Residual36369835521232785 Total41.93E+09 Coefficients Standard Errort StatP-valueLower 95%Upper 95% Lower 95.0% Upper 95.0% Intercept5841.3651328340.9220.7003260.53415-20703.232385.9-20703.232385.9 X Variable 10.4379111840.0467199.3733390.0025720.2892310.5865910.2892310.586591

6
6 The End

Similar presentations

© 2024 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google