1 Introduction What does it mean when there is a strong positive correlation between x and y ? Regression analysis aims to find a precise formula to relate.

Slides:



Advertisements
Similar presentations
Kin 304 Regression Linear Regression Least Sum of Squares
Advertisements

Chapter 12 Simple Linear Regression
6-1 Introduction To Empirical Models 6-1 Introduction To Empirical Models.
Ch11 Curve Fitting Dr. Deshi Ye
Simple Linear Regression
LINEAR REGRESSION: Evaluating Regression Models Overview Assumptions for Linear Regression Evaluating a Regression Model.
The Multiple Regression Model Prepared by Vera Tabakova, East Carolina University.
LINEAR REGRESSION: Evaluating Regression Models. Overview Assumptions for Linear Regression Evaluating a Regression Model.
The Simple Linear Regression Model: Specification and Estimation
Copyright © 2008 by the McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Managerial Economics, 9e Managerial Economics Thomas Maurice.
1-1 Regression Models  Population Deterministic Regression Model Y i =  0 +  1 X i u Y i only depends on the value of X i and no other factor can affect.
Chapter 10 Simple Regression.
Class 5: Thurs., Sep. 23 Example of using regression to make predictions and understand the likely errors in the predictions: salaries of teachers and.
REGRESSION What is Regression? What is the Regression Equation? What is the Least-Squares Solution? How is Regression Based on Correlation? What are the.
Chapter 4 Multiple Regression.
Chapter 12a Simple Linear Regression
Lecture 19: Tues., Nov. 11th R-squared (8.6.1) Review
Chapter Topics Types of Regression Models
Lecture 16 – Thurs, Oct. 30 Inference for Regression (Sections ): –Hypothesis Tests and Confidence Intervals for Intercept and Slope –Confidence.
REGRESSION Predict future scores on Y based on measured scores on X Predictions are based on a correlation from a sample where both X and Y were measured.
Correlation and Regression Analysis
Linear Regression and Correlation
Introduction to Linear Regression and Correlation Analysis
Chapter 11 Simple Regression
1 FORECASTING Regression Analysis Aslı Sencer Graduate Program in Business Information Systems.
Stats for Engineers Lecture 9. Summary From Last Time Confidence Intervals for the mean t-tables Q Student t-distribution.
Ms. Khatijahhusna Abd Rani School of Electrical System Engineering Sem II 2014/2015.
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Chapter 15 Multiple Regression n Multiple Regression Model n Least Squares Method n Multiple.
Applied Quantitative Analysis and Practices LECTURE#23 By Dr. Osman Sadiq Paracha.
Topic 10 - Linear Regression Least squares principle - pages 301 – – 309 Hypothesis tests/confidence intervals/prediction intervals for regression.
Copyright © 2005 by the McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Managerial Economics Thomas Maurice eighth edition Chapter 4.
Chapter 4 Linear Regression 1. Introduction Managerial decisions are often based on the relationship between two or more variables. For example, after.
1 Multiple Regression A single numerical response variable, Y. Multiple numerical explanatory variables, X 1, X 2,…, X k.
Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc. Chap 13-1 Introduction to Regression Analysis Regression analysis is used.
Chapter 10 For Explaining Psychological Statistics, 4th ed. by B. Cohen 1 A perfect correlation implies the ability to predict one score from another perfectly.
Multiple Regression. Simple Regression in detail Y i = β o + β 1 x i + ε i Where Y => Dependent variable X => Independent variable β o => Model parameter.
LECTURE 9 Tuesday, 24 FEBRUARY STA291 Fall Administrative 4.2 Measures of Variation (Empirical Rule) 4.4 Measures of Linear Relationship Suggested.
Economics 173 Business Statistics Lecture 10 Fall, 2001 Professor J. Petry
Linear Prediction Correlation can be used to make predictions – Values on X can be used to predict values on Y – Stronger relationships between X and Y.
Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Simple Linear Regression Analysis Chapter 13.
1 Simple Linear Regression and Correlation Least Squares Method The Model Estimating the Coefficients EXAMPLE 1: USED CAR SALES.
1 1 Slide The Simple Linear Regression Model n Simple Linear Regression Model y =  0 +  1 x +  n Simple Linear Regression Equation E( y ) =  0 + 
Lecture 10 Introduction to Linear Regression and Correlation Analysis.
Global predictors of regression fidelity A single number to characterize the overall quality of the surrogate. Equivalence measures –Coefficient of multiple.
Chapter 14 Introduction to Regression Analysis. Objectives Regression Analysis Uses of Regression Analysis Method of Least Squares Difference between.
BUSINESS MATHEMATICS & STATISTICS. Module 6 Correlation ( Lecture 28-29) Line Fitting ( Lectures 30-31) Time Series and Exponential Smoothing ( Lectures.
11-1 Copyright © 2014, 2011, and 2008 Pearson Education, Inc.
1 AAEC 4302 ADVANCED STATISTICAL METHODS IN AGRICULTURAL RESEARCH Part II: Theory and Estimation of Regression Models Chapter 5: Simple Regression Theory.
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Slides by JOHN LOUCKS St. Edward’s University.
The simple linear regression model and parameter estimation
Regression and Correlation of Data Summary
Basic Estimation Techniques
Regression Chapter 6 I Introduction to Regression
Ch12.1 Simple Linear Regression
BIVARIATE REGRESSION AND CORRELATION
Slope of the regression line:
Correlation and Simple Linear Regression
Basic Estimation Techniques
Linear Regression.
Regression Models - Introduction
No notecard for this quiz!!
Chapter 3 Statistical Concepts.
Simple Linear Regression and Correlation
Simple Linear Regression
Product moment correlation
Ch 4.1 & 4.2 Two dimensions concept
Regression and Correlation of Data
Ch3 The Two-Variable Regression Model
Regression and Correlation of Data
Correlation and Simple Linear Regression
Presentation transcript:

1 Introduction What does it mean when there is a strong positive correlation between x and y ? Regression analysis aims to find a precise formula to relate the movements of y to those of x The use of regression requires a good deal of thought and a good dose of skepticism

2 Example: Sales vs. Advertising

3

4 It is believed that sales, S, are tied to advertising, A, by a simple linear equation: What do  0 and  1 represent? How can we find  0 and  1 ?

5 Sales vs. Advertising The linear relation is usually not exact. A more realistic model: Where  0 and  1 are regression coefficients.

6 Measurement Error Properties of distribution A mean of zero Symmetry around zero An assignment of greater probability to small errors than to larger ones Errors are assumed to be: Independent Have same variance (homoscedasticity)

7 Method of Least Squares Consider the simple formula: Where the measurement errors are independent samples from N(0, ) How to find the estimators of  0 and  1 ?

8 Choosing the best line y x I II III

9 Least Squares Estimates Suggest an index to measure discrepancy between points and line Focus on vertical disparities between points and line Sum of the square of the deviations:

10 Least Squares Estimates