Inference after ANOVA, Multiple Comparisons 3/21/12 Inference after ANOVA The problem of multiple comparisons Bonferroni’s Correction Section 8.2 Professor.

Slides:



Advertisements
Similar presentations
Statistics: Unlocking the Power of Data Lock 5 STAT 101 Dr. Kari Lock Morgan ANOVA SECTION 8.1 Testing for a difference in means across multiple categories.
Advertisements

Hypothesis Testing, Synthesis
Exploratory Data Analysis I
BPS - 5th Ed. Chapter 241 One-Way Analysis of Variance: Comparing Several Means.
Multiple Regression II 4/11/12 Categorical explanatory variables Adjusted R 2 Not in book Professor Kari Lock Morgan Duke University.
Statistics: Unlocking the Power of Data Lock 5 STAT 101 Dr. Kari Lock Morgan Simple Linear Regression SECTION 2.6, 9.1 Least squares line Interpreting.
STAT 101: Data Analysis and Statistical Inference Professor Kari Lock Morgan
July 1, 2008Lecture 17 - Regression Testing1 Testing Relationships between Variables Statistics Lecture 17.
Lecture 21: Review Review a few points about regression that I went over quickly concerning coefficient of determination, regression diagnostics and transformation.
ANOVA Determining Which Means Differ in Single Factor Models Determining Which Means Differ in Single Factor Models.
1 Business 90: Business Statistics Professor David Mease Sec 03, T R 7:30-8:45AM BBC 204 Lecture 23 = Finish Chapter “Confidence Interval Estimation” (CIE)
Two Groups Too Many? Try Analysis of Variance (ANOVA)
Stat 217 – Week 10. Outline Exam 2 Lab 7 Questions on Chi-square, ANOVA, Regression  HW 7  Lab 8 Notes for Thursday’s lab Notes for final exam Notes.
STAT 101 Dr. Kari Lock Morgan Exam 2 Review.
Adminstrative Info for Final Exam Location: Steinberg Hall-Dietrich Hall 351 Time: Thursday, May 1st, 4:00-6:00 p.m. Closed book. Allowed two double-sided.
Hypothesis Testing Using The One-Sample t-Test
Simple Linear Regression Least squares line Interpreting coefficients Prediction Cautions The formal model Section 2.6, 9.1, 9.2 Professor Kari Lock Morgan.
Statistics: Unlocking the Power of Data Lock 5 STAT 101 Dr. Kari Lock Morgan Simple Linear Regression SECTIONS 9.3 Confidence and prediction intervals.
June 23, 2008Stat Lecture 13 - One Mean1 Inference for a Population Mean Confidence Intervals and Tests with unknown variance and Two- sample Tests.
Economics 173 Business Statistics Lecture 9 Fall, 2001 Professor J. Petry
Statistics: Unlocking the Power of Data Lock 5 Inference for Proportions STAT 250 Dr. Kari Lock Morgan Chapter 6.1, 6.2, 6.3, 6.7, 6.8, 6.9 Formulas for.
ANOVA 3/19/12 Mini Review of simulation versus formulas and theoretical distributions Analysis of Variance (ANOVA) to compare means: testing for a difference.
Hypothesis Testing III 2/15/12 Statistical significance Errors Power Significance and sample size Section 4.3 Professor Kari Lock Morgan Duke University.
Statistics: Unlocking the Power of Data Lock 5 Hypothesis Testing: Hypotheses STAT 101 Dr. Kari Lock Morgan SECTION 4.1 Statistical test Null and alternative.
Statistics: Unlocking the Power of Data Lock 5 STAT 101 Dr. Kari Lock Morgan 11/27/12 Multiple Regression SECTION 10.3 Categorical variables Variable.
Synthesis and Review 3/26/12 Multiple Comparisons Review of Concepts Review of Methods - Prezi Essential Synthesis 3 Professor Kari Lock Morgan Duke University.
Lecturer’s desk INTEGRATED LEARNING CENTER ILC 120 Screen Row A Row B Row C Row D Row E Row F Row G Row.
June 2, 2008Stat Lecture 18 - Review1 Final review Statistics Lecture 18.
Statistics for the Social Sciences Psychology 340 Fall 2013 Thursday, November 21 Review for Exam #4.
Chapter 13: Inference in Regression
STAT E100 Section Week 10 – Hypothesis testing, 1- Proportion, 2- Proportion – Z tests, 2- Sample T tests.
MGS 351 Introduction to Management Information Systems
Statistics: Unlocking the Power of Data Lock 5 STAT 101 Dr. Kari Lock Morgan 9/18/12 Confidence Intervals: Bootstrap Distribution SECTIONS 3.3, 3.4 Bootstrap.
Lecturer’s desk INTEGRATED LEARNING CENTER ILC 120 Screen Row A Row B Row C Row D Row E Row F Row G Row.
June 25, 2008Stat Lecture 14 - Two Means1 Comparing Means from Two Samples Statistics 111 – Lecture 14 One-Sample Inference for Proportions and.
Statistics: Unlocking the Power of Data Lock 5 STAT 101 Dr. Kari Lock Morgan 11/1/12 ANOVA SECTION 8.1 Testing for a difference in means across multiple.
Statistics: Unlocking the Power of Data Lock 5 STAT 101 Dr. Kari Lock Morgan Multiple Regression SECTIONS 9.2, 10.1, 10.2 Multiple explanatory variables.
AP Statistics: ANOVA Section 2
Statistics: Unlocking the Power of Data Lock 5 Hypothesis Testing: Cautions STAT 250 Dr. Kari Lock Morgan SECTION 4.3, 4.5 Type I and II errors (4.3) Statistical.
Multiple Regression I 4/9/12 Transformations The model Individual coefficients R 2 ANOVA for regression Residual standard error Section 9.4, 9.5 Professor.
CHAPTER 27 PART 1 Inferences for Regression. YearRate This table.
Section 10.1 Confidence Intervals
Statistics: Unlocking the Power of Data Lock 5 STAT 101 Dr. Kari Lock Morgan 11/6/12 Simple Linear Regression SECTIONS 9.1, 9.3 Inference for slope (9.1)
Statistics: Unlocking the Power of Data Lock 5 Exam 2 Review STAT 101 Dr. Kari Lock Morgan 11/13/12 Review of Chapters 5-9.
Principles of Biostatistics ANOVA. DietWeight Gain (grams) Standard910 8 Junk Food Organic Table shows weight gains for mice on 3 diets.
Statistics: Unlocking the Power of Data Lock 5 STAT 101 Dr. Kari Lock Morgan 12/6/12 Synthesis Big Picture Essential Synthesis Bayesian Inference (continued)
Chapter 12 Introduction to Analysis of Variance PowerPoint Lecture Slides Essentials of Statistics for the Behavioral Sciences Eighth Edition by Frederick.
Introduction to Statistics I MATH 1131, Summer I 2008, Department of Math. & Stat., York University.
CHAPTER 27: One-Way Analysis of Variance: Comparing Several Means
June 30, 2008Stat Lecture 16 - Regression1 Inference for relationships between variables Statistics Lecture 16.
Statistics: Unlocking the Power of Data Lock 5 STAT 101 Dr. Kari Lock Morgan 11/20/12 Multiple Regression SECTIONS 9.2, 10.1, 10.2 Multiple explanatory.
Copyright © 1998, Triola, Elementary Statistics Addison Wesley Longman 1 Assumptions 1) Sample is large (n > 30) a) Central limit theorem applies b) Can.
Statistics: Unlocking the Power of Data Lock 5 STAT 250 Dr. Kari Lock Morgan Synthesis and Review for Exam 2.
Statistics: Unlocking the Power of Data Lock 5 Inference for Means STAT 250 Dr. Kari Lock Morgan Sections 6.4, 6.5, 6.6, 6.10, 6.11, 6.12, 6.13 t-distribution.
Synthesis and Review 2/20/12 Hypothesis Tests: the big picture Randomization distributions Connecting intervals and tests Review of major topics Open Q+A.
While you wait: Enter the following in your calculator. Find the mean and sample variation of each group. Bluman, Chapter 121.
Introduction to Statistics for the Social Sciences SBS200, COMM200, GEOG200, PA200, POL200, or SOC200 Lecture Section 001, Fall 2015 Room 150 Harvill.
Jump to first page Inferring Sample Findings to the Population and Testing for Differences.
Statistics: Unlocking the Power of Data Lock 5 STAT 101 Dr. Kari Lock Morgan 11/6/12 Simple Linear Regression SECTION 2.6 Interpreting coefficients Prediction.
Hypothesis Testing Involving One Population Chapter 11.4, 11.5, 11.2.
Introduction to Statistics for the Social Sciences SBS200, COMM200, GEOG200, PA200, POL200, or SOC200 Lecture Section 001, Spring 2016 Room 150 Harvill.
AP Stats Check In Where we’ve been… Chapter 7…Chapter 8… Where we are going… Significance Tests!! –Ch 9 Tests about a population proportion –Ch 9Tests.
Statistics: Unlocking the Power of Data Lock 5 STAT 250 Dr. Kari Lock Morgan Synthesis and Review for Exam 1.
Chapter 12 Introduction to Analysis of Variance
Lecture Slides Elementary Statistics Twelfth Edition
About 45 questions / 50 points
Suggestions for Preparation
AP Stats Check In Where we’ve been… Chapter 7…Chapter 8…
While you wait Page 636 Answer all the questions on Applying Concepts for section your answers will have to be eventually turned in. You may later.
Lecture Slides Elementary Statistics Twelfth Edition
Presentation transcript:

Inference after ANOVA, Multiple Comparisons 3/21/12 Inference after ANOVA The problem of multiple comparisons Bonferroni’s Correction Section 8.2 Professor Kari Lock Morgan Duke University

Clicker: overwhelmingly positive Textbook: positive Homework: surprisingly positive Lecture: mostly positive, some complaints most common complaint: too fast not always open to questions full lecture slides not posted in advance Lab: less positive, varied complaints TA too busy, TA not helpful can do at home too long Midterm Evaluation

Project 1 (due TOMORROW, 5pm) Project 1 Homework 7 (due Monday, 3/26) Homework 7 NO LATE HOMEWORK ACCEPTED! Turn in by Friday, 3/23, 5pm to get it graded before Exam 2. Start preparing for Exam 2 (next Wednesday and Thursday) To Do

Conclusion: What have you learned? Make sure to answer the research question posed in your introduction. This is a paper, there should be text. Do not just give R output, formulas, and numbers. Project 1 Comments

Exam 2: In-class portion: Wednesday, 3/28 Lab portion: Thursday, 3/29 In-class portion: (75%) Open only to a calculator and two double sided pages of notes prepared by you Lab portion : (25%) Open to everything except communication of any form with other humans Exam 2

The emphasis will be on material we have learned since the first exam, although you are still responsible for everything we learned prior to the first exam Exam 2

On the course website, under documents: Last semester’s in-class exam, and solutions Last semester’s labs exam, and solutions Full solutions to all essential synthesis problems from Unit 3 Full solutions to all review problems from Unit 3 Full solutions to all odd problems from Chapters 7 and 8 Doing problems is the key to success!!! Practice

Work lots of practice problems! Take last year’s exams under realistic conditions (time yourself, do it all before looking at the solutions, etc.) Prepare a good cheat sheet and use it when working problems Read the corresponding sections in the book if there are concepts you are still confused about Keys to In-Class Exam Success

Primarily, make sure you know how to summarize, visualize, create an interval, and conduct a test for any one variable or relationship between two variables For practice, try doing both intervals and tests for any one or two variables in our class survey Beyond that, make sure you are comfortable with the content from the labs Open-book does NOT mean you don’t have to study. You will not have time to look up every command you need during the exam. Keys to Lab Exam Success

You have LOTS of opportunities for help! Today, 3 – 5pm (Prof Morgan) Today, 6 – 8 pm (Michael) Friday, 1:30 – 3 pm (Prof Morgan) Sunday, 5 – 7 pm (Jessica) Sunday, 7 – 9 pm (Michael) Monday, 3 – 4 pm (Prof Morgan) Monday, 4 – 6 pm (Christine) Tuesday, 3 – 6 pm (Prof Morgan) Tuesday, 6 – 8 pm (Yue) Office Hours before Exam

Cuckoo Birds Cuckoo birds lay their eggs in the nests of other birds (typically small birds). When the cuckoo baby hatches, it kicks out all the original eggs/babies If the cuckoo is lucky, the mother will raise the cuckoo as if it were her own Do cuckoo birds found in nests of different species differ?

Length of Cuckoo Eggs

BirdSample Mean Sample SD Sample Size Pied Wagtail Pipit Robin Sparrow Wren Overall Is there a significant difference between the groups? (a) Yes (b) No

Source Groups Error Total df Sum of Squares Mean Square F Statistic p-value 4.3 × ANOVA Table We have very strong evidence that average length of cuckoo eggs differs for nests of different species Equal variability Normal(ish) data

Cuckoo Birds How long are cuckoo bird eggs found in robins’ nests? Is there a significant difference between the average length of eggs found in robins’ nests and the average length of eggs found in sparrows’ nests? While we could proceed with formulas from Chapter 6 or simulation methods from Chapters 3 and 4, there are special ways of doing inference after ANOVA…

Inferences after ANOVA If the ANOVA assumption of equal variability across groups is satisfied, we can use the data from all groups to estimate variability:

Cuckoo Birds How long are cuckoo bird eggs found in robins’ nests? Give a 90% confidence interval. Is there a significant difference between the average length of eggs found in robins’ nests and the average length of eggs found in sparrows’ nests? (a)Yes (b)No BirdSample MeanSample SDSample Size Pied Wagtail Pipit Robin Sparrow Wren Overall (22.19, 22.97)

Cuckoo Birds How long are cuckoo bird eggs found in robins’ nests? Give a 90% confidence interval. t*t* We are 90% confident that the average length of Cuckoo eggs found in Robins’ nests is between and mm.

Cuckoo Birds Is there a significant difference between the average length of eggs found in robins’ nests and the average length of eggs found in sparrows’ nests? This study does not provide evidence for a difference in average mean length of Cuckoo eggs between those found in Robins and Sparrows nests.

Pairwise Comparisons Pairwise comparisons test for a difference in means between each pair of groups Only do pairwise comparisons if the overall ANOVA is significant If there are lots of categories, the number of possible pairwise comparisons grows quickly Automate the process with RStudio

Cuckoo Birds

Length of Cuckoo Eggs

Extrasensory Perception Is there such a thing as extrasensory perception (ESP), or a “sixth sense”? Do you believe in ESP or a sixth sense? (a) Yes (b) No (c) Not sure

Extrasensory Perception

How would you test whether American belief in ESP differs between current Duke students who take STAT 101 and all Americans in 2001? a)z-test b)t-test c)Chi-square test d)ANOVA

Extrasensory Perception Based on the available data, how would you test whether belief in ESP differs between 1990 and 2001? a)z-test b)t-test c)Chi-square test d)ANOVA

Summary When performing inference after ANOVA, use √MSE as an estimate for standard deviation within groups, and use n – k as the degrees of freedom for the t-distribution