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

Slides:

Advertisements

Similar presentations

Cost-Volume-Profit Analysis Managerial Accounting Prepared by Diane Tanner University of North Florida Chapter 7.

Advertisements

Simple Regression Model

Causal Forecasting by Gordon Lloyd. What will be covered? What is forecasting? What is forecasting? Methods of forecasting Methods of forecasting What.

Prepared by Diane Tanner University of North Florida Chapter 10 1 Cost Estimation.

Multiple Regression Analysis

Chapter 13 Multiple Regression

LINEAR REGRESSION: Evaluating Regression Models. Overview Standard Error of the Estimate Goodness of Fit Coefficient of Determination Regression Coefficients.

St. Louis City Crime Analysis 2015 Homicide Prediction Presented by: Kranthi Kancharla Scott Manns Eric Rodis Kenneth Stecher Sisi Yang.

Chapter 12 Multiple Regression

1 An example. 2 AirlinePercentage on time Complaints Southwest Continental Northwest US Airways United American

Chapter 12b Testing for significance—the t-test Developing confidence intervals for estimates of β 1. Testing for significance—the f-test Using Excel’s.

Basic Business Statistics, 11e © 2009 Prentice-Hall, Inc. Chap 14-1 Chapter 14 Introduction to Multiple Regression Basic Business Statistics 11 th Edition.

Chapter 4: Demand Estimation The estimation of a demand function using econometric techniques involves the following steps Identification of the variables.

Linear Regression Example Data

Managerial Accounting

McGraw-Hill/Irwin Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. Business Statistics: Communicating with Numbers By Sanjiv Jaggia.

Chapter 6 (cont.) Regression Estimation. Simple Linear Regression: review of least squares procedure 2.

Linear Regression with Excel  Brief Tutorial for Forecasting Assignment MIT Airport Systems Planning Course Shiro Yamanaka.

MANAGERIAL ECONOMICS 12th Edition

Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-1 Chapter 13 Introduction to Multiple Regression Statistics for Managers.

Quantitative Demand Analysis

Lessons Learned Discover 101 -D Theresia Wansi 1/08/09.

Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-1 Chapter 12 Simple Linear Regression Statistics for Managers Using.

Extending that Line into the Future St. Louis CMG February 12, 2008 Wayne Bell – UniGroup, Inc.

C opyright  2007 by Oxford University Press, Inc. PowerPoint Slides Prepared by Robert F. Brooker, Ph.D.Slide 1 1.

Statistics for Business and Economics 8 th Edition Chapter 11 Simple Regression Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall Ch.

Linear Trend Lines Y t = b 0 + b 1 X t Where Y t is the dependent variable being forecasted X t is the independent variable being used to explain Y. In.

OPIM 303-Lecture #8 Jose M. Cruz Assistant Professor.

Linear Trend Lines = b 0 + b 1 X t Where is the dependent variable being forecasted X t is the independent variable being used to explain Y. In Linear.

You want to examine the linear dependency of the annual sales of produce stores on their size in square footage. Sample data for seven stores were obtained.

Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 12-1 Correlation and Regression.

Chap 12-1 A Course In Business Statistics, 4th © 2006 Prentice-Hall, Inc. A Course In Business Statistics 4 th Edition Chapter 12 Introduction to Linear.

Applied Quantitative Analysis and Practices LECTURE#22 By Dr. Osman Sadiq Paracha.

Applied Quantitative Analysis and Practices LECTURE#23 By Dr. Osman Sadiq Paracha.

Ch4 Describing Relationships Between Variables. Section 4.1: Fitting a Line by Least Squares Often we want to fit a straight line to data. For example.

Project By Vishnu Narasimhan Elizabeth Stillwell Aditya Dhirani Unemployment in the United States.

C opyright  2007 by Oxford University Press, Inc. PowerPoint Slides Prepared by Robert F. Brooker, Ph.D.Slide 1 1.

Linear Trend Lines = b 0 + b 1 X t Where is the dependent variable being forecasted X t is the independent variable being used to explain Y. In Linear.

Statistics for Business and Economics 8 th Edition Chapter 11 Simple Regression Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall Ch.

Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc. Chap 13-1 Introduction to Regression Analysis Regression analysis is used.

Statistics for Business and Economics 8 th Edition Chapter 11 Simple Regression Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall Ch.

Simple Linear Regression In the previous lectures, we only focus on one random variable. In many applications, we often work with a pair of variables.

Linear Discriminant Analysis (LDA). Goal To classify observations into 2 or more groups based on k discriminant functions (Dependent variable Y is categorical.

PLC Group: Mr. Keefe Mr. Brewer Mr. Skramstad Student Reading Habits and its Impact on CST.

 Input parameters 1, 2, …, n  Values of each denoted X 1, X 2, X n  For each setting of X 1, X 2, X n observe a Y  Each set (X 1, X 2, X n,Y) is one.

Chapter 10: Determining How Costs Behave 1 Horngren 13e.

Copyright ©2011 Pearson Education, Inc. publishing as Prentice Hall 14-1 Chapter 14 Introduction to Multiple Regression Statistics for Managers using Microsoft.

Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc.. Chap 14-1 Chapter 14 Introduction to Multiple Regression Basic Business Statistics 10 th Edition.

© 2016 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license.

© 2001 Prentice-Hall, Inc.Chap 13-1 BA 201 Lecture 18 Introduction to Simple Linear Regression (Data)Data.

Engineers often: Regress data to a model  Used for assessing theory  Used for predicting  Empirical or theoretical model Use the regression of others.

Houston, Texas FAT CITY, USA Gloria Lobo-Stratton Sharon Lovdahl Dennis Glendenning.

Introduction to Multiple Regression Lecture 11. The Multiple Regression Model Idea: Examine the linear relationship between 1 dependent (Y) & 2 or more.

Regression Analysis in Microsoft Excel MS&T Physics 1135 and 2135 Labs.

Real Estate Sales Forecasting Regression Model of Pueblo neighborhood North Elizabeth Data sources from Pueblo County Website.

Statistics for Managers Using Microsoft Excel, 5e © 2008 Prentice-Hall, Inc.Chap 14-1 Statistics for Managers Using Microsoft® Excel 5th Edition Chapter.

Regression Modeling Applications in Land use and Transport.

Chapter 4 Minitab Recipe Cards. Correlation coefficients Enter the data from Example 4.1 in columns C1 and C2 of the worksheet.

Simple linear regression and correlation Regression analysis is the process of constructing a mathematical model or function that can be used to predict.

REGRESSION REVISITED. PATTERNS IN SCATTER PLOTS OR LINE GRAPHS Pattern Pattern Strength Strength Regression Line Regression Line Linear Linear y = mx.

Chapter 12 Simple Regression Statistika.  Analisis regresi adalah analisis hubungan linear antar 2 variabel random yang mempunyai hub linear,  Variabel.

Prepared by Diane Tanner University of North Florida ACG Cost Estimation 4-1.

Copyright © 2010 by the McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Managerial Economics & Business Strategy Chapter 3 Quantitative.

*Bring Money for Yearbook!

*Bring Money for Yearbook!

PowerPoint Slides Prepared by Robert F. Brooker, Ph.D. Slide 1

Principles and Worldwide Applications, 7th Edition

Jihye Chun Kyungjin Lee Rick Jantz Yang Song

S519: Evaluation of Information Systems

Simple Linear Regression

Presentation transcript:

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

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

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.

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.

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 R R Square Adjusted R Square Standard Error Observations5 ANOVA dfSSMSFSignific. F Regression11.87E Residual Total41.93E+09 Coefficients Standard Errort StatP-valueLower 95%Upper 95% Lower 95.0% Upper 95.0% Intercept X Variable

6 The End