EEM332 Lecture Slides1 EEM332 Design of Experiments En. Mohd Nazri Mahmud MPhil (Cambridge, UK) BEng (Essex, UK) Room 2.14 Ext. 6059.

Slides:



Advertisements
Similar presentations
11-1 Empirical Models Many problems in engineering and science involve exploring the relationships between two or more variables. Regression analysis.
Advertisements

More on understanding variance inflation factors (VIFk)
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?
Objectives (BPS chapter 24)
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.
Basic Business Statistics, 11e © 2009 Prentice-Hall, Inc. Chap 13-1 Chapter 13 Simple Linear Regression Basic Business Statistics 11 th Edition.
Chapter 7 Analysis of ariance Variation Inherent or Natural Variation Due to the cumulative effect of many small unavoidable causes. Also referred to.
Note 14 of 5E Statistics with Economics and Business Applications Chapter 12 Multiple Regression Analysis A brief exposition.
Announcements: Next Homework is on the Web –Due next Tuesday.
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.
Pris mot antal rum. Regression Analysis: Price versus Rooms The regression equation is Price = Rooms Predictor Coef SE Coef T P Constant.
Hypothesis Testing. Introduction Always about a population parameter Attempt to prove (or disprove) some assumption Setup: alternate hypothesis: What.
EEM332 Design of Experiments En. Mohd Nazri Mahmud
REGRESSION AND CORRELATION
Slide 1 Larger is better case (Golf Ball) Linear Model Analysis: SN ratios versus Material, Diameter, Dimples, Thickness Estimated Model Coefficients for.
Basic Business Statistics, 11e © 2009 Prentice-Hall, Inc. Chap 15-1 Chapter 15 Multiple Regression Model Building Basic Business Statistics 11 th Edition.
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”
Model selection Stepwise regression. Statement of problem A common problem is that there is a large set of candidate predictor variables. (Note: The examples.
M23- Residuals & Minitab 1  Department of ISM, University of Alabama, ResidualsResiduals A continuation of regression analysis.
OPIM 303-Lecture #8 Jose M. Cruz Assistant Professor.
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
1 © 2008 Brooks/Cole, a division of Thomson Learning, Inc. Chapter 5 Summarizing Bivariate Data.
Understanding Variation in Patient Satisfaction. All Rights Reserved, Juran Institute, Inc. Understanding Variation in Patient Satisfaction 2.PPT Variation.
Summarizing Bivariate Data
Introduction to Probability and Statistics Thirteenth Edition Chapter 12 Linear Regression and Correlation.
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.
Chap 13-1 Copyright ©2012 Pearson Education, Inc. publishing as Prentice Hall Chap 13-1 Chapter 13 Simple Linear Regression Basic Business Statistics 12.
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.
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.
STA 286 week 131 Inference for the Regression Coefficient Recall, b 0 and b 1 are the estimates of the slope β 1 and intercept β 0 of population regression.
Lecture 10 Chapter 23. Inference for regression. Objectives (PSLS Chapter 23) Inference for regression (NHST Regression Inference Award)[B level award]
Chapter 10 Correlation and Regression Lecture 1 Sections: 10.1 – 10.2.
1 1 Slide © 2003 South-Western/Thomson Learning™ Slides Prepared by JOHN S. LOUCKS St. Edward’s University.
Multiple Regression I 1 Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. Chapter 4 Multiple Regression Analysis (Part 1) Terry Dielman.
Copyright © 2004 by The McGraw-Hill Companies, Inc. All rights reserved.
Statistics and Numerical Method Part I: Statistics Week VI: Empirical Model 1/2555 สมศักดิ์ ศิวดำรงพงศ์ 1.
Copyright © 2004 by The McGraw-Hill Companies, Inc. All rights reserved.
Inference for regression - More details about simple linear regression IPS chapter 10.2 © 2006 W.H. Freeman and Company.
Chapter 12 Simple Linear Regression.
732G21/732G28/732A35 Lecture 4. Variance-covariance matrix for the regression coefficients 2.
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.
1 Chapter 5.8 What if We Have More Than Two Samples?
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.
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.
Chapter 13 Created by Bethany Stubbe and Stephan Kogitz.
Business Statistics, 4e by Ken Black
Solutions for Tutorial 3
Essentials of Statistics for Business and Economics (8e)
Business Statistics, 4e by Ken Black
Chapter Thirteen McGraw-Hill/Irwin
Presentation transcript:

EEM332 Lecture Slides1 EEM332 Design of Experiments En. Mohd Nazri Mahmud MPhil (Cambridge, UK) BEng (Essex, UK) Room 2.14 Ext. 6059

EEM332 Lecture Slides2 Agenda ANOVA with Excel and Minitab exercises

EEM332 Lecture Slides3 Example 3-1page62 Graphical examination of the data using scatter plots In Excel choose XY scatter plot In Minitab, choose >Graph>Plot>Specify X and Y variable The scatter diagram indicates increasing etch rate with power

EEM332 Lecture Slides4 Example 3-1page62 Graphical examination of the data using boxplots In Minitab, choose >Graph>Boxplots The boxplot indicates increasing etch rate with power

EEM332 Lecture Slides5 Example 3-1page62 Graphical examination of the data using Regression Model (page 86) useful to see the response at intermediate factor levels Linear Model In Excel >Insert>Chart>XYScatter and add trendline and show equations

EEM332 Lecture Slides6 Example 3-1page62 Graphical examination of the data using Regression Model useful to see the Response and intermediate factors Quadratic Model – choose Polynomial order 2

EEM332 Lecture Slides7 Example 3-1page62 Graphical examination of the data using Regression Model In Minitab, choose >Stat>Regression The boxplot indicates increasing etch rate with power Regression Analysis: Etch Rate versus RF Power The regression equation is Etch Rate = RF Power Predictor Coef SE Coef T P Constant RF Power S = R-Sq = 88.4% R-Sq(adj) = 87.8%

EEM332 Lecture Slides8 Example 3-1page62 ANOVA In Minitab, choose >Stat>ANOVA>One-way Can also tick Boxplot and Boxplot to select One-way ANOVA: Etch Rate versus RF Power Analysis of Variance for Etch Rat Source DF SS MS F P RF Power Error Total > Between treatment mean square >> within-treatment(error) mean square  F-value 66.8 > F 0.05,3,16 (3.24)  P-value is very small

EEM332 Lecture Slides9 Analysis of variance – Exercises using Minitab Question 1 The tensile strength of portland cement is being studied. Four different mixing techniques can be used economically. A completely randomised experiment was conducted and the following data collected. Mixing Technique Tensile Strength Perform ANOVA using Minitab to test the hypotheses that mixing techniques affect the tensile strength

EEM332 Lecture Slides10 Analysis of variance - Exercise Q 2 A manufacturer of television sets is interested in the effect of tube conductivity of four different types of coating for color picture tubes. The following conductivity data are obtained. Coating Type Conductivity Perform ANOVA using Minitab to test the hypotheses that coating types affect the conductivity.

EEM332 Lecture Slides11 Analysis of variance - Exercises Q 3 Four different designs for a digital circuits are being studied in order to compare the amount of noise present. The following data have been obtained. Circuit designs Noise observed Perform ANOVA using Minitab to test the hypotheses whether the noise are the same for all the four designs or not.