Week 13 Application! DPPG 8504 Sara Spowart, PhD, MPA

Slides:



Advertisements
Similar presentations
Multiple Regression and Model Building
Advertisements

1 Multiple Regression A single numerical response variable, Y. Multiple numerical explanatory variables, X 1, X 2,…, X k.
Multiple Regression Fenster Today we start on the last part of the course: multivariate analysis. Up to now we have been concerned with testing the significance.
1 Multiple Regression Response, Y (numerical) Explanatory variables, X 1, X 2, …X k (numerical) New explanatory variables can be created from existing.
LECTURE 3 Introduction to Linear Regression and Correlation Analysis
LINEAR REGRESSION: What it Is and How it Works Overview What is Bivariate Linear Regression? The Regression Equation How It’s Based on r.
LINEAR REGRESSION: What it Is and How it Works. Overview What is Bivariate Linear Regression? The Regression Equation How It’s Based on r.
Linear Regression.
Chapter 10 Simple Regression.
© 2000 Prentice-Hall, Inc. Chap Multiple Regression Models.
Multiple Regression Models. The Multiple Regression Model The relationship between one dependent & two or more independent variables is a linear function.
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 +
Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 2000 LIND MASON MARCHAL 1-1 Chapter Twelve Multiple Regression and Correlation Analysis GOALS When.
Predictive Analysis in Marketing Research
1 1 Slide © 2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.
Ch. 14: The Multiple Regression Model building
1 1 Slide © 2003 South-Western/Thomson Learning™ Slides Prepared by JOHN S. LOUCKS St. Edward’s University.
Simple Linear Regression Analysis
Multiple Regression Models
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Slides by JOHN LOUCKS & Updated by SPIROS VELIANITIS.
Example of Simple and Multiple Regression
McGraw-Hill/IrwinCopyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Simple Linear Regression Analysis Chapter 13.
Introduction to Linear Regression and Correlation Analysis
Chapter 13: Inference in Regression
Chapter 11 Simple Regression
Chapter 14 Introduction to Multiple Regression Sections 1, 2, 3, 4, 6.
Statistics for Business and Economics 8 th Edition Chapter 11 Simple Regression Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall Ch.
Copyright © 2013, 2010 and 2007 Pearson Education, Inc. Chapter Inference on the Least-Squares Regression Model and Multiple Regression 14.
Introduction to Statistics for the Social Sciences SBS200, COMM200, GEOG200, PA200, POL200, or SOC200 Lecture Section 001, Spring 2015 Room 150 Harvill.
OPIM 303-Lecture #8 Jose M. Cruz Assistant Professor.
1 1 Slide © 2016 Cengage Learning. All Rights Reserved. The equation that describes how the dependent variable y is related to the independent variables.
1 1 Slide © 2005 Thomson/South-Western Slides Prepared by JOHN S. LOUCKS St. Edward’s University Slides Prepared by JOHN S. LOUCKS St. Edward’s University.
1 1 Slide © 2003 Thomson/South-Western Chapter 13 Multiple Regression n Multiple Regression Model n Least Squares Method n Multiple Coefficient of Determination.
1 1 Slide © 2007 Thomson South-Western. All Rights Reserved OPIM 303-Lecture #9 Jose M. Cruz Assistant Professor.
1 1 Slide © 2007 Thomson South-Western. All Rights Reserved Chapter 13 Multiple Regression n Multiple Regression Model n Least Squares Method n Multiple.
1 1 Slide © 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.
1 1 Slide Multiple Regression n Multiple Regression Model n Least Squares Method n Multiple Coefficient of Determination n Model Assumptions n Testing.
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Chapter 15 Multiple Regression n Multiple Regression Model n Least Squares Method n Multiple.
Multiple Regression and Model Building Chapter 15 Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.McGraw-Hill/Irwin.
Sullivan – Fundamentals of Statistics – 2 nd Edition – Chapter 4 Section 2 – Slide 1 of 20 Chapter 4 Section 2 Least-Squares Regression.
1 1 Slide © 2014 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.
PY 603 – Advanced Statistics II TR 12:30-1:45pm 232 Gordon Palmer Hall Jamie DeCoster.
Section 4.2: Least-Squares Regression Goal: Fit a straight line to a set of points as a way to describe the relationship between the X and Y variables.
Multiple Regression BPS chapter 28 © 2006 W.H. Freeman and Company.
Chapter 13 Multiple Regression
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.
Political Science 30: Political Inquiry. Linear Regression II: Making Sense of Regression Results Interpreting SPSS regression output Coefficients for.
Multiple Regression. Simple Regression in detail Y i = β o + β 1 x i + ε i Where Y => Dependent variable X => Independent variable β o => Model parameter.
General Linear Model.
1 1 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.
1.What is Pearson’s coefficient of correlation? 2.What proportion of the variation in SAT scores is explained by variation in class sizes? 3.What is the.
LESSON 6: REGRESSION 2/21/12 EDUC 502: Introduction to Statistics.
Psychology 202a Advanced Psychological Statistics November 10, 2015.
Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Simple Linear Regression Analysis Chapter 13.
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.
© 2000 Prentice-Hall, Inc. Chap Chapter 10 Multiple Regression Models Business Statistics A First Course (2nd Edition)
Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Multiple Regression Chapter 14.
Chapter 14 Introduction to Regression Analysis. Objectives Regression Analysis Uses of Regression Analysis Method of Least Squares Difference between.
Chapter 12 Simple Regression Statistika.  Analisis regresi adalah analisis hubungan linear antar 2 variabel random yang mempunyai hub linear,  Variabel.
Introduction Many problems in Engineering, Management, Health Sciences and other Sciences involve exploring the relationships between two or more variables.
Chapter 11: Linear Regression E370, Spring From Simple Regression to Multiple Regression.
26134 Business Statistics Week 5 Tutorial
Political Science 30: Political Inquiry
Statistical Analysis of the Randomized Block Design
STA 282 Introduction to Statistics
The Multiple Regression Model
Example on the Concept of Regression . observation
24/02/11 Tutorial 2 Inferential Statistics, Statistical Modelling & Survey Methods (BS2506) Pairach Piboonrungroj (Champ)
Presentation transcript:

Week 13 Application! DPPG 8504 Sara Spowart, PhD, MPA

Today’s Agenda Pop-quiz Multiple Regression Scenario Dummy Coding Scenario

Regression Line (I will NOT ask you to calculate this outside of R Commander*) The Regression Line With one independent variable, we may write the regression equation as: Y is an observed score on the dependent variable a is the intercept b is the slope X is the observed score on the independent variable e is an error or residual We can extend this to any number of independent variables: Note: If you want to calculate by hand the equations are here at:

Regression Line For OUR purposes I want you to be able to say: Y= a + b 1 Education 1 + b 2 Safety 2 + b 3 Relaxation 3 + error This is just a ‘statistical sentence’ to indicate which independent variables you have selected to predict Y. That is the main significance of it here This statistical sentence means: “Y” is predicted by the independent variables “Education,” “Safety” and “Relaxation”

Multiple Regression Activity Please download the “Country Data” from the class website under week 13 Calculate Multiple Regression 3 times HDI= Y & Fertility and GDP = X 1 and X 2 Fertility = Y & HDI and GDP = X 1 and X 2 GDP = Y & Fertility and HDI= X 1 and X 2 1) Write ‘statistical sentences’ for the statistically significant relationships 2) Interpretation of slope coefficients: For every 1 unit increase in X i, Y increases/decreases by b i, holding other factors in the model constant* (Please memorize this)

Multiple Regression Activity Here: Y= HDI and Fertility X 1 and GDP X 2 1) First, look at overall p-value=.01 <.05 so reject the null 2) Second, GDP (X 2 ) is predictive of HDI (Y) here 3) Third, describe the relationship of GDP to HDI. For every 1 unit increase in GDP, HDI increases by , holding fertility constant.*

Multiple Regression Activity Here: Y= GDP and Fertility X 1 and HDI X 2 1) First, look at overall p-value=.02 <.05 so reject the null 2) Second, HDI (X 2 ) is predictive of GDP (Y) here 3) Third, describe the relationship of HDI to GDP. For every 1 unit increase in HDI, GDP increases by 5804 billion dollars, holding fertility constant.*

Multiple Regression Activity Here: Y= GDP and Fertility X 1 and HDI X 2 First look at overall p-value..464>.or so keep null

Survey Activity Please complete class activity listed under class materials in Week 13 Share findings with the class