Managerial Economics Demand Estimation. Scatter Diagram Regression Analysis.

Slides:

Advertisements

Similar presentations

Test of (µ 1 – µ 2 ),  1 =  2, Populations Normal Test Statistic and df = n 1 + n 2 – 2 2– )1– 2 ( 2 1 )1– 1 ( 2 where ] 2 – 1 [–

Advertisements

Managerial Economics in a Global Economy

Regression Analysis Module 3. Regression Regression is the attempt to explain the variation in a dependent variable using the variation in independent.

Learning Objectives Copyright © 2002 South-Western/Thomson Learning Data Analysis: Bivariate Correlation and Regression CHAPTER sixteen.

Learning Objectives Copyright © 2004 John Wiley & Sons, Inc. Bivariate Correlation and Regression CHAPTER Thirteen.

Learning Objectives 1 Copyright © 2002 South-Western/Thomson Learning Data Analysis: Bivariate Correlation and Regression CHAPTER sixteen.

Correlation and Regression

Definition  Regression Model  Regression Equation Y i =  0 +  1 X i ^ Given a collection of paired data, the regression equation algebraically describes.

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

Copyright © 2008 by the McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Managerial Economics, 9e Managerial Economics Thomas Maurice.

Chapter 10 Simple Regression.

Lesson #32 Simple Linear Regression. Regression is used to model and/or predict a variable; called the dependent variable, Y; based on one or more independent.

Probability & Statistics for Engineers & Scientists, by Walpole, Myers, Myers & Ye ~ Chapter 11 Notes Class notes for ISE 201 San Jose State University.

BCOR 1020 Business Statistics

MANAGERIAL ECONOMICS 12th Edition

Lecture 5 Correlation and Regression

Prepared by Robert F. Brooker, Ph.D. Copyright ©2004 by South-Western, a division of Thomson Learning. All rights reserved.Slide 1 Managerial Economics.

© Stevenson, McGraw Hill, Assoc. Prof. Sami Fethi, EMU, All Right Reserved. Regression Analysis; Chapter4 MGMT 405, POM, 2010/11. Lec Notes Chapter.

Introduction to Linear Regression and Correlation Analysis

Learning Objective Chapter 14 Correlation and Regression Analysis CHAPTER fourteen Correlation and Regression Analysis Copyright © 2000 by John Wiley &

Regression Maarten Buis Outline Recap Estimation Goodness of Fit Goodness of Fit versus Effect Size transformation of variables and effect.

Lecturer: Kem Reat, Viseth, PhD (Economics)

Chapter 8 Curve Fitting.

MANAGERIAL ECONOMICS 11 th Edition By Mark Hirschey.

Regression. Population Covariance and Correlation.

Examining Relationships in Quantitative Research

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

1 Chapter 12 Simple Linear Regression. 2 Chapter Outline  Simple Linear Regression Model  Least Squares Method  Coefficient of Determination  Model.

Chapter 5: Regression Analysis Part 1: Simple Linear Regression.

Copyright © 2005 by the McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Managerial Economics Thomas Maurice eighth edition Chapter 4.

Y X 0 X and Y are not perfectly correlated. However, there is on average a positive relationship between Y and X X1X1 X2X2.

10B11PD311 Economics REGRESSION ANALYSIS. 10B11PD311 Economics Regression Techniques and Demand Estimation Some important questions before a firm are.

Chapter 5 Demand Estimation Managerial Economics: Economic Tools for Today’s Decision Makers, 4/e By Paul Keat and Philip Young.

Correlation and Regression Basic Concepts. An Example We can hypothesize that the value of a house increases as its size increases. Said differently,

Examining Relationships in Quantitative Research

Chapter Thirteen Copyright © 2006 John Wiley & Sons, Inc. Bivariate Correlation and Regression.

Environmental Modeling Basic Testing Methods - Statistics III.

Regression Analysis. 1. To comprehend the nature of correlation analysis. 2. To understand bivariate regression analysis. 3. To become aware of the coefficient.

Lesson 14 - R Chapter 14 Review. Objectives Summarize the chapter Define the vocabulary used Complete all objectives Successfully answer any of the review.

1 Simple Linear Regression and Correlation Least Squares Method The Model Estimating the Coefficients EXAMPLE 1: USED CAR SALES.

Regression Analysis Deterministic model No chance of an error in calculating y for a given x Probabilistic model chance of an error First order linear.

Multiple Regression Analysis Regression analysis with two or more independent variables. Leads to an improvement.

AP STATISTICS LESSON 3 – 3 (DAY 2) The role of r 2 in regression.

REGRESSION AND CORRELATION SIMPLE LINEAR REGRESSION 10.2 SCATTER DIAGRAM 10.3 GRAPHICAL METHOD FOR DETERMINING REGRESSION 10.4 LEAST SQUARE METHOD.

Correlation and Regression Basic Concepts. An Example We can hypothesize that the value of a house increases as its size increases. Said differently,

1 AAEC 4302 ADVANCED STATISTICAL METHODS IN AGRICULTURAL RESEARCH Part II: Theory and Estimation of Regression Models Chapter 5: Simple Regression Theory.

Irwin/McGraw-Hill © Andrew F. Siegel, 1997 and l Chapter 9 l Simple Linear Regression 9.1 Simple Linear Regression 9.2 Scatter Diagram 9.3 Graphical.

Bivariate Regression. Bivariate Regression analyzes the relationship between two variables. Bivariate Regression analyzes the relationship between two.

Chapter 4 Demand Estimation

Regression and Correlation of Data Summary

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

Principles and Worldwide Applications, 7th Edition

REGRESSION (R2).

Basic Estimation Techniques

Micro Economics in a Global Economy

Linear Regression Bonus

Ordinary Least Squares (OLS) Regression

Simple Linear Regression

CHAPTER fourteen Correlation and Regression Analysis

BPK 304W Correlation.

Managerial Economics in a Global Economy

Chapter 4 Demand Estimation

Linear Regression.

LESSON 23: MULTIPLE REGRESSION

AP STATISTICS LESSON 3 – 3 (DAY 2)

24/02/11 Tutorial 3 Inferential Statistics, Statistical Modelling & Survey Methods (BS2506) Pairach Piboonrungroj (Champ)

Least-Squares Regression

HW# : Complete the last slide

Ch 4.1 & 4.2 Two dimensions concept

BEC 30325: MANAGERIAL ECONOMICS

Presentation transcript:

Managerial Economics Demand Estimation

Scatter Diagram Regression Analysis

Regression Line: Line of Best Fit Regression Line: Minimizes the sum of the squared vertical deviations (e t ) of each point from the regression line. Ordinary Least Squares (OLS) Method

Regression Analysis

Ordinary Least Squares (OLS) Model:

Ordinary Least Squares (OLS) Objective: Determine the slope and intercept that minimize the sum of the squared errors.

Ordinary Least Squares (OLS) Estimation Procedure

Ordinary Least Squares (OLS) Estimation Example

Ordinary Least Squares (OLS) Estimation Example

Tests of Significance Standard Error of the Slope Estimate

Tests of Significance Example Calculation

Tests of Significance Example Calculation

Tests of Significance Calculation of the t Statistic Degrees of Freedom = (n-k) = (10-2) = 8 Critical Value at 5% level =2.306

Tests of Significance Decomposition of Sum of Squares Total Variation = Explained Variation + Unexplained Variation

Tests of Significance Decomposition of Sum of Squares

Tests of Significance Coefficient of Determination

Tests of Significance Coefficient of Correlation

Multiple Regression Analysis Model:

Multiple Regression Analysis Adjusted Coefficient of Determination

Multiple Regression Analysis Analysis of Variance and F Statistic

Steps in Demand Estimation Model Specification: Identify Variables Collect Data Specify Functional Form Estimate Function Test the Results

Functional Form Specifications Linear Function: Power Function:Estimation Format: