© 2010 Pearson Prentice Hall. All rights reserved Multiple Comparisons for the Single Factor ANOVA.

Slides:



Advertisements
Similar presentations
STATISTICS HYPOTHESES TEST (I)
Advertisements

Lecture 2 ANALYSIS OF VARIANCE: AN INTRODUCTION
Inference in the Simple Regression Model
Elementary Statistics
6. Statistical Inference: Example: Anorexia study Weight measured before and after period of treatment y i = weight at end – weight at beginning For n=17.
Chi-Square and Analysis of Variance (ANOVA)
Hypothesis Tests: Two Independent Samples
“Students” t-test.
Statistical Inferences Based on Two Samples
© The McGraw-Hill Companies, Inc., Chapter 10 Testing the Difference between Means and Variances.
Chapter Thirteen The One-Way Analysis of Variance.
Ch 14 實習(2).
Lesson #24 Multiple Comparisons. When doing ANOVA, suppose we reject H 0 :  1 =  2 =  3 = … =  k Next, we want to know which means differ. This does.
Copyright © 2013 Pearson Education, Inc. All rights reserved Chapter 11 Simple Linear Regression.
Experimental Design and Analysis of Variance
Testing the Difference between Proportions Section 11.3.
STAT 2120 Tim Keaton. ANalysis Of VAriance (ANOVA) ANOVA is a generalization of the comparison of two population means In ANOVA, we compare k population.
MARE 250 Dr. Jason Turner Analysis of Variance (ANOVA)
© 2010 Pearson Prentice Hall. All rights reserved Least Squares Regression Models.
The Two Factor ANOVA © 2010 Pearson Prentice Hall. All rights reserved.
© 2010 Pearson Prentice Hall. All rights reserved The Complete Randomized Block Design.
© 2010 Pearson Prentice Hall. All rights reserved Single Factor ANOVA.
© 2010 Pearson Prentice Hall. All rights reserved Two Sample Hypothesis Testing for Means from Independent Groups.
Two Sample Hypothesis Testing for Proportions
© 2010 Pearson Prentice Hall. All rights reserved Hypothesis Testing Using a Single Sample.
© 2010 Pearson Prentice Hall. All rights reserved Two Sample Hypothesis Testing for Means from Paired or Dependent Groups.
Independent Sample T-test Formula
Mean for sample of n=10 n = 10: t = 1.361df = 9Critical value = Conclusion: accept the null hypothesis; no difference between this sample.
Analysis of Variance (ANOVA) MARE 250 Dr. Jason Turner.
Chapter 12: Analysis of Variance
Chapter 12 ANOVA.
Intermediate Applied Statistics STAT 460
1 1 Slide © 2005 Thomson/South-Western Chapter 13, Part A Analysis of Variance and Experimental Design n Introduction to Analysis of Variance n Analysis.
Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Chapter Inference on the Least-Squares Regression Model and Multiple Regression 14.
Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Chapter Comparing Three or More Means 13.
© Copyright McGraw-Hill CHAPTER 12 Analysis of Variance (ANOVA)
Statistics 11 Confidence Interval Suppose you have a sample from a population You know the sample mean is an unbiased estimate of population mean Question:
Section Copyright © 2014, 2012, 2010 Pearson Education, Inc. Lecture Slides Elementary Statistics Twelfth Edition and the Triola Statistics Series.
Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Section Inference about Two Means: Independent Samples 11.3.
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 =
Statistics for Business and Economics 8 th Edition Chapter 11 Simple Regression Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall Ch.
Chapter 15 – Analysis of Variance Math 22 Introductory Statistics.
Aim: How do we test hypotheses that compare means of two groups? HW: complete last two questions on homework slides.
Chapter 8 1-Way Analysis of Variance - Completely Randomized Design.
Slide Slide 1 Section 8-4 Testing a Claim About a Mean:  Known.
Hypothesis Testing Errors. Hypothesis Testing Suppose we believe the average systolic blood pressure of healthy adults is normally distributed with mean.
MARE 250 Dr. Jason Turner Analysis of Variance (ANOVA)
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved Lecture Slides Elementary Statistics Eleventh Edition and the Triola.
McGraw-Hill, Bluman, 7th ed., Chapter 12
McGraw-Hill, Bluman, 7th ed., Chapter 12
One-way ANOVA Example Analysis of Variance Hypotheses Model & Assumptions Analysis of Variance Multiple Comparisons Checking Assumptions.
Chapter 8 Analysis of METOC Variability. Contents 8.1. One-factor Analysis of Variance (ANOVA) 8.2. Partitioning of METOC Variability 8.3. Mathematical.
Analysis of Variance ANOVA - method used to test the equality of three or more population means Null Hypothesis - H 0 : μ 1 = μ 2 = μ 3 = μ k Alternative.
Slide Slide 1 Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley. Lecture Slides Elementary Statistics Tenth Edition and the.
MARE 250 Dr. Jason Turner Analysis of Variance (ANOVA)
24 IVMultiple Comparisons A.Contrast Among Population Means (  i ) 1. A contrast among population means is a difference among the means with appropriate.
Posthoc Comparisons finding the differences. Statistical Significance What does a statistically significant F statistic, in a Oneway ANOVA, tell us? What.
© 2010 Pearson Prentice Hall. All rights reserved Chapter Hypothesis Tests Regarding a Parameter 10.
© 2010 Pearson Prentice Hall. All rights reserved Chapter Hypothesis Tests Regarding a Parameter 10.
Introduction For inference on the difference between the means of two populations, we need samples from both populations. The basic assumptions.
Copyright © 2008 by Hawkes Learning Systems/Quant Systems, Inc.
Chapter 4. Inference about Process Quality
STAT 312 Chapter 7 - Statistical Intervals Based on a Single Sample
Comparing Three or More Means
Data Analysis and Interpretation
Chapter 12 Inference on the Least-squares Regression Line; ANOVA
Elementary Statistics
Do you know population SD? Use Z Test Are there only 2 groups to
Working with Two Populations
STATISTICS INFORMED DECISIONS USING DATA
Presentation transcript:

© 2010 Pearson Prentice Hall. All rights reserved Multiple Comparisons for the Single Factor ANOVA

13-2 When the results from a one-way ANOVA lead us to conclude that at least one population mean is different from the others, we can make additional comparisons between the means to determine which means differ significantly. The procedures for making these comparisons are called multiple comparison methods.

13-3 After rejecting the null hypothesis H 0 : 1 = 2 = ··· = k the following steps can be used to compare pairs of means for significant differences, provided that 1.There are k simple random samples from k populations. 2.The k samples are independent of each other. 3.The populations are normally distributed. 4.The populations have the same variance. Step 1: Arrange the sample means in ascending order. Tukey Intervals for Multiple Comparisons

Tukey confidence intervals The Tukey method of constructing these intervals uses the following formula:

From the constructed intervals we can determine if two means are statistically different if the confidence interval for the difference does not contain the value zero.

Parallel Example 2: Tukey Intervals Using Technology Suppose we have the measurements of body weight change for three popular diet plans after 10 weeks on the diet. The results are tabulated below: Diet ADiet BDiet C IS there evidence of a difference between diet methods and if so what is the difference?

Using technology we have the results of the ANOVA below: One-way ANOVA: Diet A, Diet B, Diet C Source DF SS MS F P Factor Error Total S = R-Sq = 78.05% R-Sq(adj) = 74.39% We have sufficient evidence at the 5% level of significance to support the claim that at least two of the diets differ with respect to the mean weight loss after 10 weeks. Solution

Tukey 95% Simultaneous Confidence Intervals All Pairwise Comparisons Diet A subtracted from: Lower Center Upper Diet B (----*----) Diet C (----*-----) Diet B subtracted from: Lower Center Upper Diet C (----*-----)

Solution An alternative to the Tukey interval comes from the Tukey tests where small test P-values signify a difference between means where we are testing the hypotheses:

Solution TukeySimultaneous Tests Response Variable Weight change All Pairwise Comparisons among Levels of Diet Diet = Diet A subtracted from: Difference SE of Adjusted Diet of Means Difference T-Value P-Value Diet B Diet C Diet = Diet B subtracted from: Difference SE of Adjusted Diet of Means Difference T-Value P-Value Diet C

Solution Interpretation: The mean change in body weight for diets A and C are not significantly different. The mean change in body weight for diet B is significantly different than diets A and C. It seems that diet B results in the highest mean weight loss after 10 weeks.