Today: Quizz 8 Friday: GLM review Monday: Exam 2.

Slides:



Advertisements
Similar presentations
Prepared by Lloyd R. Jaisingh
Advertisements

Inference for Regression
REMINDER 1) GLM Review on Friday 2) Exam II on Monday.
Analysis of variance (ANOVA)-the General Linear Model (GLM)
Part III The General Linear Model Chapter 9 Regression.
Objectives (BPS chapter 24)
Generalized Linear Models (GLM)
The Two Factor ANOVA © 2010 Pearson Prentice Hall. All rights reserved.
Statistics for Managers Using Microsoft® Excel 5th Edition
Chapter 12 Multiple Regression
Statistics 350 Lecture 16. Today Last Day: Introduction to Multiple Linear Regression Model Today: More Chapter 6.
Chapter 3 Analysis of Variance
Basic Business Statistics, 11e © 2009 Prentice-Hall, Inc. Chap 14-1 Chapter 14 Introduction to Multiple Regression Basic Business Statistics 11 th Edition.
REGRESSION AND CORRELATION
Simple Linear Regression and Correlation
Hypothesis Testing :The Difference between two population mean :
Review for Exam 2 Some important themes from Chapters 6-9 Chap. 6. Significance Tests Chap. 7: Comparing Two Groups Chap. 8: Contingency Tables (Categorical.
Chapter 7 Forecasting with Simple Regression
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-1 Chapter 13 Introduction to Multiple Regression Statistics for Managers.
Correlation & Regression
Chapter 12: Analysis of Variance
Active Learning Lecture Slides
Regression and Correlation Methods Judy Zhong Ph.D.
The Chi-Square Distribution 1. The student will be able to  Perform a Goodness of Fit hypothesis test  Perform a Test of Independence hypothesis test.
Chapter 13: Inference in Regression
STA291 Statistical Methods Lecture 27. Inference for Regression.
Copyright © 2013, 2009, and 2007, Pearson Education, Inc. Chapter 14 Comparing Groups: Analysis of Variance Methods Section 14.2 Estimating Differences.
Part IV The General Linear Model. Multiple Explanatory Variables Chapter 13.3 Fixed *Random Effects Paired t-test.
BIOL 4605/7220 Ch 13.3 Paired t-test GPT Lectures Cailin Xu October 26, 2011.
OPIM 303-Lecture #8 Jose M. Cruz Assistant Professor.
Chapter 14 Introduction to Multiple Regression
CHAPTER 14 MULTIPLE REGRESSION
Today: Lab 9ab due after lecture: CEQ Monday: Quizz 11: review Wednesday: Guest lecture – Multivariate Analysis Friday: last lecture: review – Bring questions.
Testing Multiple Means and the Analysis of Variance (§8.1, 8.2, 8.6) Situations where comparing more than two means is important. The approach to testing.
Psychology 301 Chapters & Differences Between Two Means Introduction to Analysis of Variance Multiple Comparisons.
Part III The General Linear Model Chapter 10 GLM. ANOVA.
Multivariate Analysis. One-way ANOVA Tests the difference in the means of 2 or more nominal groups Tests the difference in the means of 2 or more nominal.
BIOL 4605/7220 GPT Lectures Cailin Xu October 12, 2011 CH 9.3 Regression.
CHAPTER 11 SECTION 2 Inference for Relationships.
Section 9-1: Inference for Slope and Correlation Section 9-3: Confidence and Prediction Intervals Visit the Maths Study Centre.
Copyright © 2013, 2009, and 2007, Pearson Education, Inc. Chapter 13 Multiple Regression Section 13.3 Using Multiple Regression to Make Inferences.
Educational Research Chapter 13 Inferential Statistics Gay, Mills, and Airasian 10 th Edition.
© Copyright McGraw-Hill Correlation and Regression CHAPTER 10.
Chapter 13 Multiple Regression
Previous Lecture: Phylogenetics. Analysis of Variance This Lecture Judy Zhong Ph.D.
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.
Analysis of Covariance Combines linear regression and ANOVA Can be used to compare g treatments, after controlling for quantitative factor believed to.
PSYC 3030 Review Session April 19, Housekeeping Exam: –April 26, 2004 (Monday) –RN 203 –Use pencil, bring calculator & eraser –Make use of your.
Lecture 10: Correlation and Regression Model.
Chapter 10 The t Test for Two Independent Samples
Copyright ©2011 Pearson Education, Inc. publishing as Prentice Hall 14-1 Chapter 14 Introduction to Multiple Regression Statistics for Managers using Microsoft.
Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc.. Chap 14-1 Chapter 14 Introduction to Multiple Regression Basic Business Statistics 10 th Edition.
Handout Twelve: Design & Analysis of Covariance
ReCap Part II (Chapters 5,6,7) Data equations summarize pattern in data as a series of parameters (means, slopes). Frequency distributions, a key concept.
Introduction to Multiple Regression Lecture 11. The Multiple Regression Model Idea: Examine the linear relationship between 1 dependent (Y) & 2 or more.
Statistics for Managers Using Microsoft Excel, 5e © 2008 Prentice-Hall, Inc.Chap 14-1 Statistics for Managers Using Microsoft® Excel 5th Edition Chapter.
Jump to first page Inferring Sample Findings to the Population and Testing for Differences.
29 October 2009 MRC CBU Graduate Statistics Lectures 4: GLM: The General Linear Model - ANOVA & ANCOVA1 MRC Cognition and Brain Sciences Unit Graduate.
Université d’Ottawa / University of Ottawa 2001 Bio 4118 Applied Biostatistics L12.1 Lecture 12: Generalized Linear Models (GLM) What are they? When do.
The 2 nd to last topic this year!!.  ANOVA Testing is similar to a “two sample t- test except” that it compares more than two samples to one another.
Chapter 12: Correlation and Linear Regression 1.
Chapter 14 Introduction to Multiple Regression
Statistical Quality Control, 7th Edition by Douglas C. Montgomery.
Inference for Regression
Comparing Three or More Means
Psychology 202a Advanced Psychological Statistics
Categorical Variables
Categorical Variables
Chapter 11 Analysis of Variance
One way ANALYSIS OF VARIANCE (ANOVA)
Presentation transcript:

Today: Quizz 8 Friday: GLM review Monday: Exam 2

Part IV The General Linear Model Multiple Explanatory Variables Chapter 14 ANCOVA 1 categorical, 1 continuous

Analysis of covariance: 1 categorical 1 continuous 2 different analysis: 1. comparison of 2 regression slopes Ch Statistical control for a continuous variable within an ANOVA design Ch 14.2 ANCOVA

Part IV The General Linear Model Multiple Explanatory Variables Chapter 14.1 ANCOVA Comparison of slopes

Heterozygosity (H) of fruit flies from Yosemite Park, Dobzhansky’s investigations H is a measure of genetic variability Altitude = harsh environment Does genetic variability decrease at higher altitudes, due to stronger selection in extreme environments? GLM | ANCOVA

1. Construct Model Response variable: H (%) = inversion heterozigosity (%) Explanatory variables: 1. Altitude (km) Continuous 2. Species Drosophila pseudoobscura Drosophila persimilis

Verbal: Inversion heterozygosity changes with altitude, depending on species Graphical: 1. Construct Model

Formal:

1. Construct Model

2. Execute analysis Data in model format lm1 <- lm(H~Alt+Sp+Alt*Sp, data=dros)

2. Execute analysis grand mean species means common slope deviations from common slope species slopes Regression equations per species

3. Evaluate model a. Straight line □ Straight line model ok? b. Need to revise model? □ Errors homogeneous? c. Assumptions for computing p-values □ Errors normal? □ Errors independent?

a. Straight line □ Straight line model ok? b. Need to revise model? □ Errors homogeneous? c. Assumptions for computing p-values □ Errors normal? □ Errors independent? 3. Evaluate model

3. Evaluate model a. Straight line □ Straight line model ok? b. Need to revise model? □ Errors homogeneous? c. Assumptions for computing p-values □ Errors normal? □ Errors independent?

4. State the population and whether the sample is representative. Not enough information about how flies were collected All measurements that could have been obtained on this collection of flies, given the procedural statement

5. Decide on mode of inference. Is hypothesis testing appropriate? 6. State H A / H o pair, test statistic, distribution, tolerance for Type I error. Interaction Term: Are the gradients in heterozigosity equal between species? Is there variance due to the interaction term?

State H A / H o pair, test statistic, distribution, tolerance for Type I error. Species Term: Does the mean heterozigosity for D. persimilis differ from that of D. pseudoobscura?

State H A / H o pair, test statistic, distribution, tolerance for Type I error. Altitude Term: Is the slope less than zero? More specific hypotheses?

6. State H A / H o pair, test statistic, distribution, tolerance for Type I error. Test Statistic Distribution of test statitstic Tolerance for Type I error

7. ANOVA From multiple regression lecture (Ch 12) Remember Type I SS: sequential sums of squares partitioning of SS is done in the order the terms are written in the model Type III SS: adjusted sums of squares SS allocated to each term when entered last into the model, i.e. controlled for the rest of the variables Minitab provides Type III R: use Anova{cars}, eg: library(cars); Anova(lm1,type=3)

7. ANOVA Anova(lm1,type=3)

8. Decide whether to recompute p-value Assumptions met, skip step

9. Declare decision about terms Interaction term p=0.003< α =0.05 Reject H 0  The rate of decrease in heterozygosity with altitude differs between species F sp*alt =15.33 df=1,10 p=0.003 No sense in checking if common slope = 0 Appropriate to check if slope for each species = 0

10. Report and interpret parameters of biological interest Let’s examine species separately D. persimilis H = 0.58 – Alt D. pseudoobscura No Δ with Alt mean(H pseu ) = 68.6 %

Part IV The General Linear Model Multiple Explanatory Variables Chapter 14.2 ANCOVA Statistical control

Crawley 1993 Response variable: Fruit production (mg) Explanatory variables: 1. Plant size (root diameter) 2. Grazed? Yes OR No We are interested in the effect of grazing on fruit production, controlled for the effect of plant size GLM | ANCOVA

Quizz 8 Good luck! Clock