10.12 Page 344 Two Sample t-test regarding (2) population means.

Slides:



Advertisements
Similar presentations
Chapter 11 Analysis of Variance
Advertisements

Chapter 16 Introduction to Nonparametric Statistics
Chapter 12 ANALYSIS OF VARIANCE.
© 2010 Pearson Prentice Hall. All rights reserved Single Factor ANOVA.
Statistics for Managers Using Microsoft® Excel 5th Edition
Chapter 11 Analysis of Variance
Chapter Topics The Completely Randomized Model: One-Factor Analysis of Variance F-Test for Difference in c Means The Tukey-Kramer Procedure ANOVA Assumptions.
Chapter 3 Analysis of Variance
Statistics for Managers Using Microsoft® Excel 5th Edition
Chapter 17 Analysis of Variance
= == Critical Value = 1.64 X = 177  = 170 S = 16 N = 25 Z =
Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Statistics for Business and Economics 7 th Edition Chapter 15 Analysis of Variance.
Chapter 11 Analysis of Variance
Copyright ©2011 Pearson Education 11-1 Chapter 11 Analysis of Variance Statistics for Managers using Microsoft Excel 6 th Global Edition.
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 10-1 Chapter 10 Analysis of Variance Statistics for Managers Using Microsoft.
Chap 10-1 Analysis of Variance. Chap 10-2 Overview Analysis of Variance (ANOVA) F-test Tukey- Kramer test One-Way ANOVA Two-Way ANOVA Interaction Effects.
Copyright ©2011 Pearson Education, Inc. publishing as Prentice Hall 11-1 Chapter 11 Analysis of Variance Statistics for Managers using Microsoft Excel.
Copyright ©2011 Pearson Education, Inc. publishing as Prentice Hall 12-1 Business Statistics: A Decision-Making Approach 8 th Edition Chapter 12 Analysis.
F-Test ( ANOVA ) & Two-Way ANOVA
Statistics for Business and Economics Chapter 8 Design of Experiments and Analysis of Variance.
INFERENTIAL STATISTICS: Analysis Of Variance ANOVA
© 2003 Prentice-Hall, Inc.Chap 11-1 Analysis of Variance IE 340/440 PROCESS IMPROVEMENT THROUGH PLANNED EXPERIMENTATION Dr. Xueping Li University of Tennessee.
Chapter 9 Hypothesis Testing and Estimation for Two Population Parameters.
Chapter 10 Two-Sample Tests and One-Way ANOVA
12-1 Chapter Twelve McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved.
© 2002 Prentice-Hall, Inc.Chap 9-1 Statistics for Managers Using Microsoft Excel 3 rd Edition Chapter 9 Analysis of Variance.
Chapter 11 HYPOTHESIS TESTING USING THE ONE-WAY ANALYSIS OF VARIANCE.
© Copyright McGraw-Hill CHAPTER 12 Analysis of Variance (ANOVA)
Chapter 10 Analysis of Variance.
ANOVA (Analysis of Variance) by Aziza Munir
Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc. Chap th Lesson Analysis of Variance.
ENGR 610 Applied Statistics Fall Week 9
Business Statistics, A First Course (4e) © 2006 Prentice-Hall, Inc. Chap 10-1 Chapter 10 Two-Sample Tests and One-Way ANOVA Business Statistics, A First.
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 10-1 Chapter 10 Analysis of Variance Statistics for Managers Using Microsoft.
INTRODUCTION TO ANALYSIS OF VARIANCE (ANOVA). COURSE CONTENT WHAT IS ANOVA DIFFERENT TYPES OF ANOVA ANOVA THEORY WORKED EXAMPLE IN EXCEL –GENERATING THE.
Chapter 19 Analysis of Variance (ANOVA). ANOVA How to test a null hypothesis that the means of more than two populations are equal. H 0 :  1 =  2 =
Comparing Three or More Means ANOVA (One-Way Analysis of Variance)
One-Way ANOVA ANOVA = Analysis of Variance This is a technique used to analyze the results of an experiment when you have more than two groups.
Lecture 9-1 Analysis of Variance
Chapter 13 - ANOVA. ANOVA Be able to explain in general terms and using an example what a one-way ANOVA is (370). Know the purpose of the one-way ANOVA.
Analysis of Variance (One Factor). ANOVA Analysis of Variance Tests whether differences exist among population means categorized by only one factor or.
ANOVA ANOVA is used when more than two groups are compared In order to conduct an ANOVA, several assumptions must be made – The population from which the.
12-1 Chapter Twelve McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved.
Chapter 11 Analysis of Variance. 11.1: The Completely Randomized Design: One-Way Analysis of Variance vocabulary –completely randomized –groups –factors.
Business Statistics: A First Course (3rd Edition)
Chapter 4 Analysis of Variance
Chap 11-1 A Course In Business Statistics, 4th © 2006 Prentice-Hall, Inc. A Course In Business Statistics 4 th Edition Chapter 11 Analysis of Variance.
CHAPTER 12 ANALYSIS OF VARIANCE Prem Mann, Introductory Statistics, 7/E Copyright © 2010 John Wiley & Sons. All right reserved.
Business Statistics, A First Course (4e) © 2006 Prentice-Hall, Inc. Chap 10-1 Chapter 10 Two-Sample Tests and One-Way ANOVA Business Statistics, A First.
Copyright © 2016, 2013, 2010 Pearson Education, Inc. Chapter 10, Slide 1 Two-Sample Tests and One-Way ANOVA Chapter 10.
Formula for Linear Regression y = bx + a Y variable plotted on vertical axis. X variable plotted on horizontal axis. Slope or the change in y for every.
ENGR 610 Applied Statistics Fall Week 8 Marshall University CITE Jack Smith.
Chapter 8 Analysis of METOC Variability. Contents 8.1. One-factor Analysis of Variance (ANOVA) 8.2. Partitioning of METOC Variability 8.3. Mathematical.
 List the characteristics of the F distribution.  Conduct a test of hypothesis to determine whether the variances of two populations are equal.  Discuss.
DSCI 346 Yamasaki Lecture 4 ANalysis Of Variance.
Chapter 11 Analysis of Variance
Chapter 11 Created by Bethany Stubbe and Stephan Kogitz.
Chapter 10 Two-Sample Tests and One-Way ANOVA.
Statistics for Managers Using Microsoft Excel 3rd Edition
Factorial Experiments
Characteristics of F-Distribution
Post Hoc Tests on One-Way ANOVA
Post Hoc Tests on One-Way ANOVA
Chapter 10 Two-Sample Tests and One-Way ANOVA.
Chapter 11 Analysis of Variance
Two-Way Analysis of Variance
Chapter 15 Analysis of Variance
Presentation transcript:

10.12 Page 344 Two Sample t-test regarding (2) population means

10.12 Page 344 ANOVA Method

10.12 Page 344 ANOVA Method

Think of the F distribution as the t distribution squared. All F-tests are one-tail tests. For a 2-tailed t-test, the area in the two-tails are combined. All F values are positive. Notice (from the first slide) t*= , and ( ) 2 =17.09

11.10 Page 344 n=30 observations c=5 groups j=6 observations in each group Fcrit=F 0.05,4,25 =2.76 There is evidence of a difference in the mean rating of the five advertisements. MSA MSW The single factor is the Ad influencing the Rating.

SSW, measure of variation within group SST, measure of TOTAL variation SSA, measure of variation in means among groups SSW mean group A mean group B mean group C mean group D mean group E SSA

Tukey-Kramer Procedure for Comparing all Pairs If the absolute value of the mean difference in a pair is greater than the Critical Range (CR) value, the difference is significant. The CR is obtained from the Q, Studentized Range distribution, with df num =c and df denom =n-c. A,B0.333 A,C6.667* A,D9.000* A,E2.667 B,C6.333* B,D8.667* B,E2.333 C,D2.333 C,E4.000 D,E6.333*

MSW df = n-c c

One-Way ANOVA Summary SourcedfSSMSF Amongc-1SSAMSA F*=MSA/MSW Withinn-cSSWMSW Fcrit=F ,c-1,n-c Totaln-1SST Main Hypothesis F distribution test, df num =c-1, df denom =n-c T-K all pairs test Q distribution test, df num =c, df denom =n-c Test regarding the difference between population means of multiple groups. One factor of influence. Equal variance assumption.

Homework a and b Perform the T-K procedure manually and in NCSS. If you have time, try to obtain SSA, SSW, and SST manually with the use of Excel. Be able to Compute MSA and MSW from SSA and SSW. Compute F* and Fcrit and interpret. Obtain Q Critical Range value and perform the T-K procedure to compare means of pairs.