More on understanding variance inflation factors (VIFk)

Slides:



Advertisements
Similar presentations
Chapter Twelve Multiple Regression and Model Building McGraw-Hill/Irwin Copyright © 2004 by The McGraw-Hill Companies, Inc. All rights reserved.
Advertisements

Qualitative predictor variables
Multicollinearity.
1 Outliers and Influential Observations KNN Ch. 10 (pp )
Chicago Insurance Redlining Example Were insurance companies in Chicago denying insurance in neighborhoods based on race?
1 Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. Summarizing Bivariate Data Introduction to Linear Regression.
DATA ANALYSIS Making Sense of Data ZAIDA RAHAYU YET.
Quantitative Methods Using more than one explanatory variable.
Every achievement originates from the seed of determination. 1Random Effect.
Introduction to Regression Analysis Straight lines, fitted values, residual values, sums of squares, relation to the analysis of variance.
Slide 1 Larger is better case (Golf Ball) Linear Model Analysis: SN ratios versus Material, Diameter, Dimples, Thickness Estimated Model Coefficients for.
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.
STA302/ week 111 Multicollinearity Multicollinearity occurs when explanatory variables are highly correlated, in which case, it is difficult or impossible.
Variable selection and model building Part II. Statement of situation A common situation is that there is a large set of candidate predictor variables.
Chapter 12: Linear Regression 1. Introduction Regression analysis and Analysis of variance are the two most widely used statistical procedures. Regression.
Regression Continued: Functional Form LIR 832. Topics for the Evening 1. Qualitative Variables 2. Non-linear Estimation.
Completing the ANOVA From the Summary Statistics.
Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 12-1 Correlation and Regression.
An alternative approach to testing for a linear association The Analysis of Variance (ANOVA) Table.
Detecting and reducing multicollinearity. Detecting multicollinearity.
Copyright ©2011 Nelson Education Limited Linear Regression and Correlation CHAPTER 12.
Regression Lesson 11. The General Linear Model n Relationship b/n predictor & outcome variables form straight line l Correlation, regression, t-tests,
Solutions to Tutorial 5 Problems Source Sum of Squares df Mean Square F-test Regression Residual Total ANOVA Table Variable.
Sequential sums of squares … or … extra sums of squares.
1 Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. Summarizing Bivariate Data Non-linear Regression Example.
Lack of Fit (LOF) Test A formal F test for checking whether a specific type of regression function adequately fits the data.
Multiple regression. Example: Brain and body size predictive of intelligence? Sample of n = 38 college students Response (Y): intelligence based on the.
Statistics and Numerical Method Part I: Statistics Week VI: Empirical Model 1/2555 สมศักดิ์ ศิวดำรงพงศ์ 1.
Regression through the origin
732G21/732G28/732A35 Lecture 4. Variance-covariance matrix for the regression coefficients 2.
5-1 MGMG 522 : Session #5 Multicollinearity (Ch. 8)
Multiple Regression II 1 Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. Chapter 4 Multiple Regression Analysis (Part 2) Terry Dielman.
Variable selection and model building Part I. Statement of situation A common situation is that there is a large set of candidate predictor variables.
Multicollinearity. Multicollinearity (or intercorrelation) exists when at least some of the predictor variables are correlated among themselves. In observational.
Design and Analysis of Experiments (7) Response Surface Methods and Designs (2) Kyung-Ho Park.
Interaction regression models. What is an additive model? A regression model with p-1 predictor variables contains additive effects if the response function.
Agenda 1.Exam 2 Review 2.Regression a.Prediction b.Polynomial Regression.
732G21/732G28/732A35 Lecture 6. Example second-order model with one predictor 2 Electricity consumption (Y)Home size (X)
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.
Analysis of variance approach to regression analysis … an (alternative) approach to testing for a linear association.
1 Multiple Regression. 2 Model There are many explanatory variables or independent variables x 1, x 2,…,x p that are linear related to the response variable.
Model selection and model building. Model selection Selection of predictor variables.
Design and Analysis of Experiments
Chapter 20 Linear and Multiple Regression
Examining Relationships
Introduction to Regression Lecture 6.2
Least Square Regression
Chapter 13 Created by Bethany Stubbe and Stephan Kogitz.
Least Square Regression
The slope, explained variance, residuals
Lecture 12 More Examples for SLR More Examples for MLR 9/19/2018
9/19/2018 ST3131, Lecture 6.
Business Statistics, 4e by Ken Black
Lecture 18 Outline: 1. Role of Variables in a Regression Equation
Cases of F-test Problems with Examples
Inference for Regression Lines
Model Selection II: datasets with several explanatory variables
Solutions for Tutorial 3
Solutions of Tutorial 10 SSE df RMS Cp Radjsq SSE1 F Xs c).
QM222 Class 15 Section D1 Review for test Multicollinearity
Lecture 5 732G21/732G28/732A35 Detta är en generell mall för att göra PowerPoint presentationer enligt LiUs grafiska profil. Du skriver in din.
Solution 9 1. a) From the matrix plot, 1) The assumption about linearity seems ok; 2).The assumption about measurement errors can not be checked at this.
Multiple Regression Chapter 14.
Business Statistics, 4e by Ken Black
Solutions of Tutorial 9 SSE df RMS Cp Radjsq SSE1 F Xs c).
Essentials of Statistics for Business and Economics (8e)
Business Statistics, 4e by Ken Black
Presentation transcript:

More on understanding variance inflation factors (VIFk)

Cement example

Pearson correlation of x2 and x4 = -0.973 The regression equation is x4 = 80.4 - 1.05 x2 Predictor Coef SE Coef T P Constant 80.396 3.777 21.28 0.000 x2 -1.04657 0.07492 -13.97 0.000 S = 4.038 R-Sq = 94.7% R-Sq(adj) = 94.2% The regression equation is x2 = 75.3 - 0.905 x4 Predictor Coef SE Coef T P Constant 75.289 2.204 34.16 0.000 x4 -0.90452 0.06475 -13.97 0.000 S = 3.754 R-Sq = 94.7% R-Sq(adj) = 94.2% Pearson correlation of x2 and x4 = -0.973

Regress y on x2 The regression equation is y = 57.4 + 0.789 x2 Predictor Coef SE Coef T P Constant 57.424 8.491 6.76 0.000 x2 0.7891 0.1684 4.69 0.001 S = 9.077 R-Sq = 66.6% R-Sq(adj) = 63.6% Analysis of Variance Source DF SS MS F P Regression 1 1809.4 1809.4 21.96 0.001 Residual Error 11 906.3 82.4 Total 12 2715.8

Regress y on x4 The regression equation is y = 118 - 0.738 x4 Predictor Coef SE Coef T P Constant 117.568 5.262 22.34 0.000 x4 -0.7382 0.1546 -4.77 0.001 S = 8.964 R-Sq = 67.5% R-Sq(adj) = 64.5% Analysis of Variance Source DF SS MS F P Regression 1 1831.9 1831.9 22.80 0.001 Residual Error 11 883.9 80.4 Total 12 2715.8

Regress y on x2 and x4 The regression equation is y = 94.2 + 0.311 x2 - 0.457 x4 Predictor Coef SE Coef T P VIF Constant 94.16 56.63 1.66 0.127 x2 0.3109 0.7486 0.42 0.687 18.7 x4 -0.4569 0.6960 -0.66 0.526 18.7 S = 9.321 R-Sq = 68.0% R-Sq(adj) = 61.6% Analysis of Variance Source DF SS MS F P Regression 2 1846.88 923.44 10.63 0.003 Residual Error 10 868.88 86.89 Total 12 2715.76

Is the variance of b4 inflated by a factor of 18.7? almost ….

Is the variance of b2 inflated by a factor of 18.7? again almost ….

Variance inflation factor VIFk The variance inflation factor quantifies “how much the variance of the estimated regression coefficient is inflated by the existence of multicollinearity.” The theory… The estimate…

Variance inflation factor VIFk To get the theoretical VIF4, , that Minitab reports, we need to multiply the ratio of the variance estimates by

Is the variance of b4 inflated by a factor of 18.7?

Is the variance of b2 inflated by a factor of 18.7?