1 Interactions Between Independent Variables (SW Section 8.3)

Slides:



Advertisements
Similar presentations
Quality Series (Sample Slides)
Advertisements

Copyright © 2011 Pearson Addison-Wesley. All rights reserved. Chapter 6 Linear Regression with Multiple Regressors.
Linear Regression with Multiple Regressors
1 Nonlinear Regression Functions (SW Chapter 8). 2 The TestScore – STR relation looks linear (maybe)…
Creating Graphs on Saturn GOPTIONS DEVICE = png HTITLE=2 HTEXT=1.5 GSFMODE = replace; PROC REG DATA=agebp; MODEL sbp = age; PLOT sbp*age; RUN; This will.
Logistic Regression and Odds Ratios
Regression with a Binary Dependent Variable
Chapter 7 Hypothesis Tests and Confidence Intervals in Multiple Regression.
Linear Regression with One Regression
Introduction to Econometrics The Statistical Analysis of Economic (and related) Data.
Introduction to Econometrics The Statistical Analysis of Economic (and related) Data.
Statistics Lecture 20. Last Day…completed 5.1 Today Section 5.2 Next Day: Parts of Section 5.3 and 5.4.
Chapter 8 Nonlinear Regression Functions. 2 Nonlinear Regression Functions (SW Chapter 8)
POLYNOMIAL REGRESSION MODELS. One-Variable Polynomial Models Recall with one variable the first step is to plot the y values vs. x to assist in hypothesizing.
Analyzing quantitative data – section III Week 10 Lecture 1.
POLYNOMIAL REGRESSION MODELS. One-Variable Polynomial Models Recall with one variable the first step is to plot the y values vs. x to assist in hypothesizing.
1 Lecture 25 (Dec.4) In the last lecture, we covered 1. Log-log specification 2. Interaction term This lecture introduces you to 1. Interaction term.
Statistics 350 Lecture 17. Today Last Day: Introduction to Multiple Linear Regression Model Today: More Chapter 6.
Leedy and Ormrod Ch. 11 Gray Ch. 14
Hypothesis Tests and Confidence Intervals in Multiple Regressors
Nonlinear Regression Functions
Chapter 8 Nonlinear Regression Functions.
Chapter 14 Introduction to Multiple Regression Sections 1, 2, 3, 4, 6.
Chapter 14 – Correlation and Simple Regression Math 22 Introductory Statistics.
16-1 Linear Trend The long term trend of many business series often approximates a straight line.
STAT 1301 Chapter 8 Scatter Plots, Correlation. For Regression Unit You Should Know n How to plot points n Equation of a line Y = mX + b m = slope b =
BASIC DATA ANALYSIS AND STATISTICS R. SHAPIRO American University in Cairo June 3-6, 2012 Motivation, Intuition, and Numerology (AUCShapiroPresent1.ppt)
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.
Introduction to Econometrics CMD. Class size vs. student achievement Policy question: What is the effect on test scores (or some other outcome measure)
Multivariate Data Analysis Chapter 5 – Discrimination Analysis and Logistic Regression.
Logistic Regression July 28, 2008 Ivan Katchanovski, Ph.D. POL 242Y-Y.
Chapter 5 Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals.
1 Hypothesis Tests & Confidence Intervals (SW Ch. 7) 1.For a single coefficient 2.For multiple coefficients 3.Other types of hypotheses involving multiple.
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.
A medical researcher wishes to determine how the dosage (in mg) of a drug affects the heart rate of the patient. DosageHeart rate
Scatter Diagrams Objective: Draw and interpret scatter diagrams. Distinguish between linear and nonlinear relations. Use a graphing utility to find the.
Correlation and Regression. Section 9.1  Correlation is a relationship between 2 variables.  Data is often represented by ordered pairs (x, y) and.
ECON 338/ENVR 305 CLICKER QUESTIONS Statistics – Question Set #8 (from Chapter 10)
Click to edit Master title style Midterm 3 Wednesday, June 10, 1:10pm.
Section 4.2 Building Linear Models from Data. OBJECTIVE 1.
[title of your research project] [author] INST 381 Fall 2015.
Multiple Regression  Similar to simple regression, but with more than one independent variable R 2 has same interpretation R 2 has same interpretation.
Chapter 6 Introduction to Multiple Regression. 2 Outline 1. Omitted variable bias 2. Causality and regression analysis 3. Multiple regression and OLS.
Logistic Regression Saed Sayad 1www.ismartsoft.com.
Unit 3 Section : Regression  Regression – statistical method used to describe the nature of the relationship between variables.  Positive.
Section 9.3 Measures of Regression and Prediction Intervals.
Topics, Summer 2008 Day 1. Introduction Day 2. Samples and populations Day 3. Evaluating relationships Scatterplots and correlation Day 4. Regression and.
732G21/732G28/732A35 Lecture 3. Properties of the model errors ε 4. ε are assumed to be normally distributed
Correlations: Linear Relationships Data What kind of measures are used? interval, ratio nominal Correlation Analysis: Pearson’s r (ordinal scales use Spearman’s.
Analysis and Interpretation: Multiple Variables Simultaneously
Linear Regression with One Regression
Building Linear Models from Data
مبررات إدخال الحاسوب في رياض الأطفال
2. Find the equation of line of regression
Review of Statistics (SW Chapters 3)
Correlation and Regression
Bivariate Linear Regression July 14, 2008
Introduction to Econometrics
Today (2/23/16) Learning objectives:
Merve denizci nazlıgül, M.s.
Two-way analysis of variance (ANOVA)
21twelveinteractive.com/ twitter.com/21twelveI/ facebook.com/21twelveinteractive/ linkedin.com/company/21twelve-interactive/ pinterest.com/21twelveinteractive/
Correlation and Regression
Multiple Linear Regression
Section 5 Multiple Regression.
Determine the type of correlation between the variables.
Regression III.
Regression and Categorical Predictors
JMP Example 5 Use the previous yield data from different dissolution temperatures. Make a model that describes the effect of temperature on the yield.
Presentation transcript:

1 Interactions Between Independent Variables (SW Section 8.3)

2 (a) Interactions of two binary vars

3 Interpreting the coefficients

4 Example

5 (b) Interactions of continuous and binary variables

6 Binary-continuous interactions: the two regression lines

7 Example: TestScore, STR, and HiEL (=1 if PctEL > 10)

8 Testing Hypotheses about Interaction Effects

9 (c) Interactions of continuous vars

10 Example: TestScore, STR, PctEL

11 Testing Hypotheses about Interaction Effects

12 Application: Nonlinear Effects on Test Scores of the Student-Teacher Ratio (SW Section 8.4)

13 1. Are there different effects for different STR levels?

14 2. Are there interactions between PctEL and STR?

15 What is a good “base” specification?

16

17 Tests of joint hypotheses:

18 Interpreting the regression functions via plots:

19 Comparing the regressions with interactions:

20 Application: Berg Dale & Krueger (QJE, 2002)

21 Application: Berg Dale & Krueger (QJE, 2002)

22 Application: Berg Dale & Krueger (QJE, 2002)

23 Application: Berg Dale & Krueger (QJE, 2002)