Slide 1 Larger is better case (Golf Ball) Linear Model Analysis: SN ratios versus Material, Diameter, Dimples, Thickness Estimated Model Coefficients for.

Slides:



Advertisements
Similar presentations
Qualitative predictor variables
Advertisements

Multicollinearity.
More on understanding variance inflation factors (VIFk)
1 Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. Summarizing Bivariate Data Introduction to Linear Regression.
Nested Designs Study vs Control Site. Nested Experiments In some two-factor experiments the level of one factor, say B, is not “cross” or “cross classified”
Design and Analysis of Experiments
Design and Analysis of Experiments Dr. Tai-Yue Wang Department of Industrial and Information Management National Cheng Kung University Tainan, TAIWAN,
Design and Analysis of Experiments Dr. Tai-Yue Wang Department of Industrial and Information Management National Cheng Kung University Tainan, TAIWAN,
LINEAR REGRESSION: Evaluating Regression Models. Overview Standard Error of the Estimate Goodness of Fit Coefficient of Determination Regression Coefficients.
DATA ANALYSIS Making Sense of Data ZAIDA RAHAYU YET.
Quantitative Methods Using more than one explanatory variable.
Note 14 of 5E Statistics with Economics and Business Applications Chapter 12 Multiple Regression Analysis A brief exposition.
Quantitative Methods Using more than one explanatory variable.
Every achievement originates from the seed of determination. 1Random Effect.
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.
Basic Business Statistics, 11e © 2009 Prentice-Hall, Inc. Chap 14-1 Chapter 14 Introduction to Multiple Regression Basic Business Statistics 11 th Edition.
Part 18: Regression Modeling 18-1/44 Statistics and Data Analysis Professor William Greene Stern School of Business IOMS Department Department of Economics.
EEM332 Lecture Slides1 EEM332 Design of Experiments En. Mohd Nazri Mahmud MPhil (Cambridge, UK) BEng (Essex, UK) Room 2.14 Ext
Polynomial regression models Possible models for when the response function is “curved”
Hypothesis tests for slopes in multiple linear regression model Using the general linear test and sequential sums of squares.
A (second-order) multiple regression model with interaction terms.
23-1 Analysis of Covariance (Chapter 16) A procedure for comparing treatment means that incorporates information on a quantitative explanatory variable,
Using the National Repository of Online Courses to Increase the Effectiveness of Developmental Mathematics Instruction Jan Case Jacksonville State University.
Chapter 14 Multiple Regression Models. 2  A general additive multiple regression model, which relates a dependent variable y to k predictor variables.
Business Statistics, 4e, by Ken Black. © 2003 John Wiley & Sons Business Statistics, 4e by Ken Black Chapter 15 Building Multiple Regression Models.
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.
Chapter 11 Linear Regression Straight Lines, Least-Squares and More Chapter 11A Can you pick out the straight lines and find the least-square?
An alternative approach to testing for a linear association The Analysis of Variance (ANOVA) Table.
Detecting and reducing multicollinearity. Detecting multicollinearity.
1 Lecture 4 Main Tasks Today 1. Review of Lecture 3 2. Accuracy of the LS estimators 3. Significance Tests of the Parameters 4. Confidence Interval 5.
Copyright ©2011 Nelson Education Limited Linear Regression and Correlation CHAPTER 12.
Diploma in Statistics Design and Analysis of Experiments Lecture 2.11 Design and Analysis of Experiments Lecture Review of Lecture Randomised.
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 Nested (Hierarchical) Designs In certain experiments the levels of one factor (eg. Factor B) are similar but not identical for different levels of another.
1 Every achievement originates from the seed of determination.
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.
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.
1 Always be contented, be grateful, be understanding and be compassionate.
Business Statistics, 4e, by Ken Black. © 2003 John Wiley & Sons Business Statistics, 4e by Ken Black Chapter 14 Multiple Regression Analysis.
Statistics and Numerical Method Part I: Statistics Week VI: Empirical Model 1/2555 สมศักดิ์ ศิวดำรงพงศ์ 1.
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.
Multiple Regression II 1 Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. Chapter 4 Multiple Regression Analysis (Part 2) Terry Dielman.
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.
Joyful mood is a meritorious deed that cheers up people around you like the showering of cool spring breeze.
Designs for Experiments with More Than One Factor When the experimenter is interested in the effect of multiple factors on a response a factorial design.
732G21/732G28/732A35 Lecture 6. Example second-order model with one predictor 2 Electricity consumption (Y)Home size (X)
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.
Diploma in Statistics Design and Analysis of Experiments Lecture 2.21 © 2010 Michael Stuart Design and Analysis of Experiments Lecture Review of.
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.
General Full Factorial Design
Introduction to Regression Lecture 6.2
Cases of F-test Problems with Examples
Model Selection II: datasets with several explanatory variables
Solutions for Tutorial 3
Using more than one explanatory variable
Business Statistics, 4e by Ken Black
Multivariate Models Regression.
Essentials of Statistics for Business and Economics (8e)
Presentation transcript:

Slide 1 Larger is better case (Golf Ball) Linear Model Analysis: SN ratios versus Material, Diameter, Dimples, Thickness Estimated Model Coefficients for SN ratios Term Coef SE Coef T P Constant Material Liquid Diameter Dimples Thicknes Material*Diameter Liquid S = R-Sq = 99.2% R-Sq(adj) = 97.2% Linear Model Analysis and Analysis of Variance (ANOVA)

Slide 2 Analysis of Variance for SN ratios Source DF Seq SS Adj SS Adj MS F P Material Diameter Dimples Thickness Material*Diameter Residual Error Total

Slide 3 Interpreting the results The order of the coefficients by absolute value indicates the relative importance of each factor to the response; the factor with the biggest coefficient has the greatest impact. The sequential and adjusted sums of squares in the analysis of variance table also indicate the relative importance of each factor; the factor with the biggest sum of squares has the greatest impact. These results mirror the factor ranks in the response tables. In this example, you generated results for S/N ratios and means. For S/N ratios, all the factors and the interaction terms are significant at an a-level of For means, core material (p=0.045), core diameter (p=0.024), and the interaction of material with diameter (p=0.06) are significant because their p- values are less than However, because both factors are involved in the interaction, you need to understand the interaction before you can consider the effect of each factor individually.

Slide 4 Source : Minitab Reference Manual