Agenda 1.Exam 2 Review 2.Regression a.Prediction b.Polynomial Regression.

Slides:

Advertisements

Similar presentations

Topic 12: Multiple Linear Regression

Advertisements

More on understanding variance inflation factors (VIFk)

Chapter 12 Simple Linear Regression

13- 1 Chapter Thirteen McGraw-Hill/Irwin © 2005 The McGraw-Hill Companies, Inc., All Rights Reserved.

Chicago Insurance Redlining Example Were insurance companies in Chicago denying insurance in neighborhoods based on race?

Ch11 Curve Fitting Dr. Deshi Ye

1 Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. Simple Linear Regression Estimates for single and mean responses.

1 1 Slide 統計學 Spring 2004 授課教師：統計系余清祥日期： 2004 年 5 月 11 日第十二週：建立迴歸模型.

Statistical Analysis of the Regression Point Displacement Design (RPD)

Economics 310 Lecture 6 Test for a Regression. Basic Hypothesis.

Note 14 of 5E Statistics with Economics and Business Applications Chapter 12 Multiple Regression Analysis A brief exposition.

Multivariate Data Analysis Chapter 4 – Multiple Regression.

Every achievement originates from the seed of determination. 1Random Effect.

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

Linear Regression MARE 250 Dr. Jason Turner.

Pris mot antal rum. Regression Analysis: Price versus Rooms The regression equation is Price = Rooms Predictor Coef SE Coef T P Constant.

1.The independent variables do not form a linearly dependent set--i.e. the explanatory variables are not perfectly correlated. 2.Homoscedasticity --the.

Part 18: Regression Modeling 18-1/44 Statistics and Data Analysis Professor William Greene Stern School of Business IOMS Department Department of Economics.

Correlation 1. Correlation - degree to which variables are associated or covary. (Changes in the value of one tends to be associated with changes in the.

Polynomial regression models Possible models for when the response function is “curved”

Descriptive measures of the strength of a linear association r-squared and the (Pearson) correlation coefficient r.

1 1 Slide © 2003 South-Western/Thomson Learning™ Slides Prepared by JOHN S. LOUCKS St. Edward’s University.

Part 3: Regression and Correlation 3-1/41 Regression Models Professor William Greene Stern School of Business IOMS Department Department of Economics.

Introduction to Linear Regression and Correlation Analysis

Regression Analysis and Multiple Regression

Outlook for Commercial Aircraft McDonnell Douglas (Made in 1995)

1 1 Slide © 2003 Thomson/South-Western Chapter 13 Multiple Regression n Multiple Regression Model n Least Squares Method n Multiple Coefficient of Determination.

Business Statistics, 4e, by Ken Black. © 2003 John Wiley & Sons Business Statistics, 4e by Ken Black Chapter 15 Building Multiple Regression Models.

Chapter 12: Linear Regression 1. Introduction Regression analysis and Analysis of variance are the two most widely used statistical procedures. Regression.

Name: Angelica F. White WEMBA10. Teach students how to make sound decisions and recommendations that are based on reliable quantitative information During.

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

Introduction to Linear Regression

TEAS prep course trends YOUR FUTURE BEGINS TODAY Joel Collazo, MD Maria E Guzman, MPM.

Chap 14-1 Copyright ©2012 Pearson Education, Inc. publishing as Prentice Hall Chap 14-1 Chapter 14 Introduction to Multiple Regression Basic Business Statistics.

Detecting and reducing multicollinearity. Detecting multicollinearity.

Chapter 4 Linear Regression 1. Introduction Managerial decisions are often based on the relationship between two or more variables. For example, after.

Copyright ©2011 Nelson Education Limited Linear Regression and Correlation CHAPTER 12.

Solutions to Tutorial 5 Problems Source Sum of Squares df Mean Square F-test Regression Residual Total ANOVA Table Variable.

Inference for regression - More details about simple linear regression IPS chapter 10.2 © 2006 W.H. Freeman and Company.

14- 1 Chapter Fourteen McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved.

1 Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. Summarizing Bivariate Data Non-linear Regression Example.

The McGraw-Hill Companies, Inc. 2006McGraw-Hill/Irwin DSS-ESTIMATING COSTS.

Lack of Fit (LOF) Test A formal F test for checking whether a specific type of regression function adequately fits the data.

Correlation and Regression: The Need to Knows Correlation is a statistical technique: tells you if scores on variable X are related to scores on variable.

Scatter Diagrams scatter plot scatter diagram A scatter plot is a graph that may be used to represent the relationship between two variables. Also referred.

Business Statistics, 4e, by Ken Black. © 2003 John Wiley & Sons Business Statistics, 4e by Ken Black Chapter 14 Multiple Regression Analysis.

Regression. Height Weight How much would an adult female weigh if she were 5 feet tall? She could weigh varying amounts – in other words, there is a distribution.

Inference for regression - More details about simple linear regression IPS chapter 10.2 © 2006 W.H. Freeman and Company.

732G21/732G28/732A35 Lecture 4. Variance-covariance matrix for the regression coefficients 2.

Multicollinearity. Multicollinearity (or intercorrelation) exists when at least some of the predictor variables are correlated among themselves. In observational.

Interaction regression models. What is an additive model? A regression model with p-1 predictor variables contains additive effects if the response function.

732G21/732G28/732A35 Lecture 6. Example second-order model with one predictor 2 Electricity consumption (Y)Home size (X)

Correlation and Regression Elementary Statistics Larson Farber Chapter 9 Hours of Training Accidents.

Chapter 26: Inference for Slope. Height Weight How much would an adult female weigh if she were 5 feet tall? She could weigh varying amounts – in other.

Simple linear regression. What is simple linear regression? A way of evaluating the relationship between two continuous variables. One variable is regarded.

Simple linear regression. What is simple linear regression? A way of evaluating the relationship between two continuous variables. One variable is regarded.

Descriptive measures of the degree of linear association R-squared and correlation.

INTRODUCTION TO MULTIPLE REGRESSION MULTIPLE REGRESSION MODEL 11.2 MULTIPLE COEFFICIENT OF DETERMINATION 11.3 MODEL ASSUMPTIONS 11.4 TEST OF SIGNIFICANCE.

David Housman for Math 323 Probability and Statistics Class 05 Ion Sensitive Electrodes.

Announcements There’s an in class exam one week from today (4/30). It will not include ANOVA or regression. On Thursday, I will list covered material and.

Introduction to Regression Lecture 6.2

Simple Linear Regression

Cases of F-test Problems with Examples

Inference for Regression Lines

Solutions for Tutorial 3

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

The Multiple Regression Model

Business Statistics, 4e by Ken Black

Section 5 Multiple Regression.

Presentation transcript:

Agenda 1.Exam 2 Review 2.Regression a.Prediction b.Polynomial Regression

Prediction  One of the objectives of regression was to be able to predict the behavior of the dependent variable.  Prediction: Providing estimates of values of the dependent variable by using the explanatory regression equation: OR:

 First need to establish that the model is a good model with strong explanatory power.  We can only use prediction in the region of the data used in the estimation process.  Replace X in the equation with the value for which you want to predict the dependent variable. Prediction (cont.)

Example #1: The regression model: Y = X 1.Is the relationship between X and Y positive or negative? 2.If X is 9, what is y? 3.If X changes one unit, how much does Y change? 4.If X is 0, what is y?

Example #2: Y = X X 2 where X 1 is miles driven, X 2 is no of deliveries and Y is hours of drive time. 1. Predict the total drive time of a driver who needs to make 3 deliveries and travel 70 miles. 2. Predict the total drive time of a drive who still drives 70 miles but now makes 4 deliveries?

Polynomial Regression  If the relationship between the dependent and an independent variable is not linear, but curvilinear, then using polynomials may improve the model. Y=  0 +  1 X +  2 X 2 +  3 X  m X m X1X1 Y X1X1 Y

Polynomial Regression Example The polynomial regression equation is: SALES = ADVERT ADV 2 Predictor Coef Stdev t-ratio p Constant ADVERT ADV R-sq = 95.9% R-sq(adj) = 95.4% Analysis of Variance: SOURCE DF SS MS F p Regression Error Total

Polynomial Regression (cont.) SALES = ADVERT ADV 2 1.Test whether or not the coefficient for ADV 2 is significant. 2.Predict what sales will be when advertising is 7 (in thousands). 3.Predict what sales will be when advertising is 15 (in thousands). 4.Predict what sales will be when advertising is 23 (in thousands).