Multiple Regression What is our research question?  How does IV1, IV2, IV3 influence the DV What unique contribution to variation in the DV does IV1 make.

Slides:



Advertisements
Similar presentations
Prepared by Sarah Perry Johnson
Advertisements

Data Analysis: Relationships Continued Regression
The Regression Equation  A predicted value on the DV in the bi-variate case is found with the following formula: Ŷ = a + B (X1)
Correlation and Linear Regression.
1 Multiple Regression A single numerical response variable, Y. Multiple numerical explanatory variables, X 1, X 2,…, X k.
Regression single and multiple. Overview Defined: A model for predicting one variable from other variable(s). Variables:IV(s) is continuous, DV is continuous.
Bivariate Regression CJ 526 Statistical Analysis in Criminal Justice.
T-Test (difference of means test) T-Test = used to compare means between two groups. Level of measurement: –DV (Interval/Ratio) –IV (Nominal—groups)
Statistical Analysis SC504/HS927 Spring Term 2008 Session 5: Week 20: 15 th February OLS (2): assessing goodness of fit, extension to multiple regression.
RESEARCH STATISTICS Jobayer Hossain Larry Holmes, Jr November 6, 2008 Examining Relationship of Variables.
ANOVA  Used to test difference of means between 3 or more groups. Assumptions: Independent samples Normal distribution Equal Variance.
Elaboration Elaboration extends our knowledge about an association to see if it continues or changes under different situations, that is, when you introduce.
Ch. 14: The Multiple Regression Model building
Multiple Regression Research Methods and Statistics.
Multiple Regression – Basic Relationships
Week 14 Chapter 16 – Partial Correlation and Multiple Regression and Correlation.
Linear Regression.  Uses correlations  Predicts value of one variable from the value of another  ***computes UKNOWN outcomes from present, known outcomes.
SW388R7 Data Analysis & Computers II Slide 1 Multiple Regression – Basic Relationships Purpose of multiple regression Different types of multiple regression.
SPSS Statistical Package for Social Sciences Multiple Regression Department of Psychology California State University Northridge
CHAPTER 5 REGRESSION Discovering Statistics Using SPSS.
Lecture 15 Basics of Regression Analysis
Chapter 12 Correlation and Regression Part III: Additional Hypothesis Tests Renee R. Ha, Ph.D. James C. Ha, Ph.D Integrative Statistics for the Social.
Elements of Multiple Regression Analysis: Two Independent Variables Yong Sept
Regression Analysis. Scatter plots Regression analysis requires interval and ratio-level data. To see if your data fits the models of regression, it is.
Copyright © 2010 Pearson Education, Inc Chapter Seventeen Correlation and Regression.
Chapter 12 Examining Relationships in Quantitative Research Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin.
Chapter 18 Some Other (Important) Statistical Procedures You Should Know About Part IV Significantly Different: Using Inferential Statistics.
The Goal of MLR  Types of research questions answered through MLR analysis:  How accurately can something be predicted with a set of IV’s? (ex. predicting.
Path Analysis. Remember What Multiple Regression Tells Us How each individual IV is related to the DV The total amount of variance explained in a DV Multiple.
Multiple Linear Regression. Purpose To analyze the relationship between a single dependent variable and several independent variables.
Multiple Regression Lab Chapter Topics Multiple Linear Regression Effects Levels of Measurement Dummy Variables 2.
© Buddy Freeman, 2015 Multiple Linear Regression (MLR) Testing the additional contribution made by adding an independent variable.
Department of Cognitive Science Michael J. Kalsher Adv. Experimental Methods & Statistics PSYC 4310 / COGS 6310 Regression 1 PSYC 4310/6310 Advanced Experimental.
CORRELATION: Correlation analysis Correlation analysis is used to measure the strength of association (linear relationship) between two quantitative variables.
Chapter 16 Data Analysis: Testing for Associations.
Correlation & Regression Correlation does not specify which variable is the IV & which is the DV.  Simply states that two variables are correlated. Hr:There.
Political Science 30: Political Inquiry. Linear Regression II: Making Sense of Regression Results Interpreting SPSS regression output Coefficients for.
September 18-19, 2006 – Denver, Colorado Sponsored by the U.S. Department of Housing and Urban Development Conducting and interpreting multivariate analyses.
Simple Linear Regression (OLS). Types of Correlation Positive correlationNegative correlationNo correlation.
Analysis Overheads1 Analyzing Heterogeneous Distributions: Multiple Regression Analysis Analog to the ANOVA is restricted to a single categorical between.
Examining Relationships in Quantitative Research
Copyright © 2010 Pearson Education, Inc Chapter Seventeen Correlation and Regression.
© 2006 by The McGraw-Hill Companies, Inc. All rights reserved. 1 Chapter 12 Testing for Relationships Tests of linear relationships –Correlation 2 continuous.
STA302: Regression Analysis. Statistics Objective: To draw reasonable conclusions from noisy numerical data Entry point: Study relationships between variables.
28. Multiple regression The Practice of Statistics in the Life Sciences Second Edition.
1/15/2016Marketing Research2  How do you test the covariation between two continuous variables?  Most typically:  One independent variable  and: 
Ch15: Multiple Regression 3 Nov 2011 BUSI275 Dr. Sean Ho HW7 due Tues Please download: 17-Hawlins.xls 17-Hawlins.xls.
Multiple Regression Analysis Regression analysis with two or more independent variables. Leads to an improvement.
Research Methodology Lecture No :26 (Hypothesis Testing – Relationship)
Multiple Independent Variables POLS 300 Butz. Multivariate Analysis Problem with bivariate analysis in nonexperimental designs: –Spuriousness and Causality.
DISCRIMINANT ANALYSIS. Discriminant Analysis  Discriminant analysis builds a predictive model for group membership. The model is composed of a discriminant.
Ch 12 Prediction/Regression Part 3: Nov 30, 2006.
Michael J. Kalsher PSYCHOMETRICS MGMT 6971 Regression 1 PSYC 4310 Advanced Experimental Methods and Statistics © 2014, Michael Kalsher.
Multivariate Analysis 6/23/ Introduction Defined as “all statistical techniques which simultaneously analyze more than two variables on a sample.
Regression Analysis.
Simple Bivariate Regression
Lecture 10 Regression Analysis
Bivariate & Multivariate Regression Analysis
Multiple Regression: I
REGRESSION (R2).
Correlation, Bivariate Regression, and Multiple Regression
Political Science 30: Political Inquiry
Regression Analysis.
Week 14 Chapter 16 – Partial Correlation and Multiple Regression and Correlation.
Multiple Regression – Part II
Example 1 5. Use SPSS output ANOVAb Model Sum of Squares df
Prediction/Regression
Prediction/Regression
Regression Analysis.
Regression Part II.
Presentation transcript:

Multiple Regression What is our research question?  How does IV1, IV2, IV3 influence the DV What unique contribution to variation in the DV does IV1 make when holding IV2 & IV3 constant? What unique contribution to the DV does IV2 make when holding IV1 & IV3 constant?

Example: TVhours regressed onto Educ, Age, Income Hr1: As education increases TV viewing decreases. Hr2: As income increases TV viewing decreases. Hr3: As age increases TV viewing increases.

Unique & Shared Variation

Output Important Statistics: R-squared = The amount of variation explained by all of the IVs taken together. AVOVA Box F Test=Tells us if the regression model is a good fit for this data. T-Test= Significance test for each Beta.

How to read B & Beta: B is the unstandardized beta.  Tells us the amount of change in the DV for a one unite change an IV, holding all other IVs constant. Beta is the standardized beta.  Tells us about the strength of an IV on the DV holding all other IVs constant.  The largest Beta has the strongest impact on the IV. Beta can range from -1 to +1 with 0 indicating no relationship.

SPSS Output See overhead