Part III The General Linear Model. Multiple Explanatory Variables Chapter 12 Multiple Regression.

Slides:



Advertisements
Similar presentations
Quantitative Methods Interactions - getting more complex.
Advertisements

1 1 Chapter 5: Multiple Regression 5.1 Fitting a Multiple Regression Model 5.2 Fitting a Multiple Regression Model with Interactions 5.3 Generating and.
Factorial ANOVA More than one categorical explanatory variable.
CHAPTER 8 MULTIPLE REGRESSION ANALYSIS: THE PROBLEM OF INFERENCE
1 Multiple Regression A single numerical response variable, Y. Multiple numerical explanatory variables, X 1, X 2,…, X k.
Part III The General Linear Model Chapter 9 Regression.
1 Multiple Regression Response, Y (numerical) Explanatory variables, X 1, X 2, …X k (numerical) New explanatory variables can be created from existing.
Statistics 350 Lecture 16. Today Last Day: Introduction to Multiple Linear Regression Model Today: More Chapter 6.
CHAPTER 4 ECONOMETRICS x x x x x Multiple Regression = more than one explanatory variable Independent variables are X 2 and X 3. Y i = B 1 + B 2 X 2i +
Chapter 4 Multiple Regression. 4.1 Introduction.
1 BA 275 Quantitative Business Methods Simple Linear Regression Introduction Case Study: Housing Prices Agenda.
Chapter 5. Operations on Multiple R. V.'s 1 Chapter 5. Operations on Multiple Random Variables 0. Introduction 1. Expected Value of a Function of Random.
Statistics 350 Lecture 17. Today Last Day: Introduction to Multiple Linear Regression Model Today: More Chapter 6.
1 Chapter 17: Introduction to Regression. 2 Introduction to Linear Regression The Pearson correlation measures the degree to which a set of data points.
Chapter 6 (cont.) Regression Estimation. Simple Linear Regression: review of least squares procedure 2.
Review for Final Exam Some important themes from Chapters 9-11 Final exam covers these chapters, but implicitly tests the entire course, because we use.
Chapter 11 Simple Regression
Chapter 14 Introduction to Multiple Regression Sections 1, 2, 3, 4, 6.
Coefficient of Determination R2
Today: Quizz 8 Friday: GLM review Monday: Exam 2.
Part IV The General Linear Model. Multiple Explanatory Variables Chapter 13.3 Fixed *Random Effects Paired t-test.
Section 4.2 Regression Equations and Predictions.
Chapter 1: Introduction to Chemistry. I. Chemistry A. Definition: study of composition of matter and changes to it B. Matter: material making up everything.
Chapter 16 The Elaboration Model Key Terms. Descriptive statistics Statistical computations describing either the characteristics of a sample or the relationship.
Factorial ANOVA More than one categorical explanatory variable STA305 Spring 2014.
Section 5.2: Linear Regression: Fitting a Line to Bivariate Data.
Chapter 17 Partial Correlation and Multiple Regression and Correlation.
BIOL 4605/7220 GPT Lectures Cailin Xu October 12, 2011 CH 9.3 Regression.
Equations of Lines Given Two Points In addition to level 3.0 and above and beyond what was taught in class, the student may: · Make connection.
© Buddy Freeman, 2015 Multiple Linear Regression (MLR) Testing the additional contribution made by adding an independent variable.
MS. Energy 07-PS3-2 I Can … Develop a model to describe that when the arrangement of objects interacting at a distance changes, different amount of potential.
Chapter 6 Simple Regression Introduction Fundamental questions – Is there a relationship between two random variables and how strong is it? – Can.
Stat 112 Notes 6 Today: –Chapter 4.1 (Introduction to Multiple Regression)
Power Point Slides by Ronald J. Shope in collaboration with John W. Creswell Chapter 12 Correlational Designs.
Statistical Data Analysis 2010/2011 M. de Gunst Lecture 10.
Chapter 17.1 Poisson Regression Classic Poisson Example Number of deaths by horse kick, for each of 16 corps in the Prussian army, from 1875 to 1894.
1 Response Surface A Response surface model is a special type of multiple regression model with: Explanatory variables Interaction variables Squared variables.
Week of March 23 Partial correlations Semipartial correlations
Topics, Summer 2008 Day 1. Introduction Day 2. Samples and populations Day 3. Evaluating relationships Scatterplots and correlation Day 4. Regression and.
CHAPTER 10 & 13 Correlation and Regression Instructor: Alaa saud Note: This PowerPoint is only a summary and your main source should be the book.
Section Copyright © 2015, 2011, 2008 Pearson Education, Inc. Lecture Slides Essentials of Statistics 5 th Edition and the Triola Statistics Series.
Analysis and Interpretation: Multiple Variables Simultaneously
Covariance/ Correlation
SIMPLE LINEAR REGRESSION MODEL
Covariance/ Correlation
Covariance/ Correlation
Correlation and Regression-II
Multiple Regression A curvilinear relationship between one variable and the values of two or more other independent variables. Y = intercept + (slope1.
Lecture Slides Elementary Statistics Twelfth Edition
Multiple Linear Regression
Chapter 1: Introduction
Simple Linear Regression
Lesson 3.1 & 3.2 Proportional Relationships
Lecture Slides Elementary Statistics Twelfth Edition
Multiple Linear Regression
Performing a regression analysis
Covariance/ Correlation
Sihua Peng, PhD Shanghai Ocean University
Models, parameters and GLMs
Chapter 13 Multiple Regression
Bivariate Data credits.
Correlation & Trend Lines
Cases. Simple Regression Linear Multiple Regression.
Statistics 101 CORRELATION Section 3.2.
Interactive Graphic Organizers
Models, parameters and GLMs
Chapter 14 Multiple Regression
Interactive Graphic Organizers
Interactive Graphic Organizers
Confidence and Prediction Intervals
Presentation transcript:

Part III The General Linear Model. Multiple Explanatory Variables Chapter 12 Multiple Regression

Introduction

GLM | Multiple Regression Pcorn example…again Example from Snedecor and Cochran (1989) Interested in the relationship between: – Phosphorus content of corn and phosphorus (organic and inorganic) levels in soil samples.

1. Construct Model Verbal: Plant available phosphorus depends on the amount of both organic and inorganic soil phosphorus Graphical:

1. Construct Model Verbal: Plant available phosphorus depends on the amount of both organic and inorganic soil phosphorus Graphical:

1. Construct Model Formal: Start with individual explanatory variables:

1. Construct Model Formal: Now we construct a model with both explanatory variables

1. Construct Model

Partial Regression

1. Construct Model Formal: Now we construct a model with both explanatory variables

1. Construct Model Formal:

1. Construct Model Formal: Finally, we add an interaction term Investigate potential interactive effects on the response variable

2. Execute analysis mr <- lm(Pcorn~ioP+oP+ioP*oP, data=corn) ioPoPPcorn

3. Evaluate Model □ Straight line model ok? □ Errors homogeneous? □ Errors normal? □ Errors independent?

4.State the population and whether the sample is representative. 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. – Separate statement for each explanatory variable

4.State the population and whether the sample is representative. 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. – Separate statement for each explanatory variable var

7. ANOVA n = 17

8. Recompute p-value if necessary. Assumptions met, skip 9. Declare decision about model terms.

Present parameter estimates along with CL – Pcorn = oP ioP ioP*oP Organic and inorganic soil phosphorus have interactive effects on phosphorus content of corn. If we wish to look at the effects of soil phosphorus on corn phosphorus content we need to know both organic and inorganic concentrations in the soil. We need to use the interaction term to compute the expected levels of corn phosphorus. 10. Report and interpret parameters of biological interest.