Dependent (Criterion) Variable – Academic Success: Academic Major Grade Point Average (Major_GPA) Independent (Predictor) Variables: Socio Economic Status.

Slides:



Advertisements
Similar presentations
Topics: Multiple Regression Analysis (MRA)
Advertisements

Hierarchical Linear Modeling: An Introduction & Applications in Organizational Research Michael C. Rodriguez.
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.
Chapter 14 The Simple Linear Regression Model. I. Introduction We want to develop a model that hopes to successfully explain the relationship between.
1 Multiple Regression A single numerical response variable, Y. Multiple numerical explanatory variables, X 1, X 2,…, X k.
Multiple Linear Regression Introduction. Multiple Regression One continuous Y, two or more X variables. X variables may be continuous or dichotomous 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.
Regression With Categorical Variables. Overview Regression with Categorical Predictors Logistic Regression.
Maternal Mind-Mindedness in the First Year Predicts Acquisition of Internal State Language and Symbolic Play at Age 2 Charles Fernyhough, Elizabeth Meins,
Chapter 17 Making Sense of Advanced Statistical Procedures in Research Articles.
LINEAR REGRESSION: What it Is and How it Works Overview What is Bivariate Linear Regression? The Regression Equation How It’s Based on r.
MULTIPLE REGRESSION. OVERVIEW What Makes it Multiple? What Makes it Multiple? Additional Assumptions Additional Assumptions Methods of Entering Variables.
State vs Trait Constructs. Criterion vs Norm referenced n Criterion reference = compares to established standard, well defined objectives n Norm referenced.
LINEAR REGRESSION: What it Is and How it Works. Overview What is Bivariate Linear Regression? The Regression Equation How It’s Based on r.
Discriminant Analysis To describe multiple regression analysis and multiple discriminant analysis. Discriminant Analysis.
Bivariate Regression CJ 526 Statistical Analysis in Criminal Justice.
Statistics 350 Lecture 16. Today Last Day: Introduction to Multiple Linear Regression Model Today: More Chapter 6.
Introduction the General Linear Model (GLM) l what “model,” “linear” & “general” mean l bivariate, univariate & multivariate GLModels l kinds of variables.
Effect of Selection Ratio on Predictor Utility Reject Accept Predictor Score Criterion Performance r =.40 Selection Cutoff Score sr =.50sr =.10sr =.95.
Multiple Regression. Want to find the best linear relationship between a dependent variable, Y, (Price), and 3 independent variables X 1 (Sq. Feet), X.
Correlation 1. Correlation - degree to which variables are associated or covary. (Changes in the value of one tends to be associated with changes in the.
Multiple Regression Research Methods and Statistics.
Multiple Regression – Basic Relationships
SPSS Statistical Package for Social Sciences Multiple Regression Department of Psychology California State University Northridge
State vs Trait Constructs. Project question 4 n Does your test measure a state or a trait?
Business Research Methods William G. Zikmund Chapter 24 Multivariate 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
Multiple Linear Regression. Purpose To analyze the relationship between a single dependent variable and several independent variables.
Then click the box for Normal probability plot. In the box labeled Standardized Residual Plots, first click the checkbox for Histogram, Multiple Linear.
Multiple Regression Lab Chapter Topics Multiple Linear Regression Effects Levels of Measurement Dummy Variables 2.
Part IV Significantly Different Using Inferential Statistics Chapter 15 Using Linear Regression Predicting Who’ll Win the Super Bowl.
Part IV Significantly Different: Using Inferential Statistics
Practical Statistics Regression. There are six statistics that will answer 90% of all questions! 1. Descriptive 2. Chi-square 3. Z-tests 4. Comparison.
Are the number of bedrooms and number of bathrooms significant predictors of monthly rent in the multiple regression model we estimated in class? Jill.
Aron, Aron, & Coups, Statistics for the Behavioral and Social Sciences: A Brief Course (3e), © 2005 Prentice Hall Chapter 12 Making Sense of Advanced Statistical.
College Prep Stats. x is the independent variable (predictor variable) ^ y = b 0 + b 1 x ^ y = mx + b b 0 = y - intercept b 1 = slope y is the dependent.
Welcome to MM570 Psychological Statistics Unit 4 Seminar Dr. Srabasti Dutta.
Correlation and Regression: The Need to Knows Correlation is a statistical technique: tells you if scores on variable X are related to scores on variable.
© 2006 by The McGraw-Hill Companies, Inc. All rights reserved. 1 Chapter 12 Testing for Relationships Tests of linear relationships –Correlation 2 continuous.
Psychology 202a Advanced Psychological Statistics November 12, 2015.
Statistics for Psychology CHAPTER SIXTH EDITION Statistics for Psychology, Sixth Edition Arthur Aron | Elliot J. Coups | Elaine N. Aron Copyright © 2013.
Regression Handout Spring 2015 WFC, FWC, verbal expression of emotion and psychological strain.
Multiple Regression. PSYC 6130, PROF. J. ELDER 2 Multiple Regression Multiple regression extends linear regression to allow for 2 or more independent.
Multiple Regression David A. Kenny January 12, 2014.
Outline of Today’s Discussion 1.Seeing the big picture in MR: Prediction 2.Starting SPSS on the Different Models: Stepwise versus Hierarchical 3.Interpreting.
Welcome to MM570 Psychological Statistics Unit 4 Seminar Dr. Bob Lockwood.
Applied Quantitative Analysis and Practices LECTURE#28 By Dr. Osman Sadiq Paracha.
Unit 7 Statistics: Multivariate Analysis of Variance (MANOVA) & Discriminant Functional Analysis (DFA) Chat until class starts.
Week of March 23 Partial correlations Semipartial correlations
Correlations: Linear Relationships Data What kind of measures are used? interval, ratio nominal Correlation Analysis: Pearson’s r (ordinal scales use Spearman’s.
(Slides not created solely by me – the internet is a wonderful tool) SW388R7 Data Analysis & Compute rs II Slide 1.
COLLEGE ADMISSION TEST SCORES Student IDReadingWritingMathTotal S S S S S
Multiple Regression Scott Hudson January 24, 2011.
Chapter 11 REGRESSION Multiple Regression  Uses  Explanation  Prediction.
Topics: Multiple Regression Analysis (MRA)
Center of Statistical Analysis Correlation and Regression Analysis
Eastern Michigan University
Correlations NOT causal relationship between variables predictive.
Multivariate Data Reduction and Meta-analysis
Making Sense of Advanced Statistical Procedures in Research Articles
بحث في التحليل الاحصائي SPSS بعنوان :
Understanding Research Results: Description and Correlation
Shudong Wang, NWEA Liru Zhang, Delaware DOE G. Gage Kingsbury, NWEA
Simple Linear Regression
Incremental Partitioning of Variance (aka Hierarchical Regression)
Multiple Linear Regression
Cases. Simple Regression Linear Multiple Regression.
Bonus Slide!!! Things to “Carefully Consider”
Business Statistics - QBM117
Presentation transcript:

Dependent (Criterion) Variable – Academic Success: Academic Major Grade Point Average (Major_GPA) Independent (Predictor) Variables: Socio Economic Status when entering college (SES) Age when entering college (Age) High School Grade Point Average (HS_GPA) ACT composite score (ACT_Comp) Model 1 (Block 1, Control) Variables – Personal: Socio Economic Status when entering college (SES) Age when entering college (Age) Model 2 Change (Block 2) Variables – Ability/Aptitude: High School Grade Point Average (HS_GPA) ACT composite score (ACT_Comp) EPS 625 – Intermediate Statistics Block Entry (Hierarchical) Multiple Linear Regression Example II – Academic Success

Total (Model 2 – Full) R 2 =.672 and is significant F(4, 495) = , p <.001

Control Variables (Model 1) R 2 =.023 and is significant F(2, 497) = 5.835, p <.01

Block 2 (Model 2, Change) R 2 Change =.649 and is significant F Change(2, 495) = , p <.001