ANOVA GenderManagement Level Average Salary MaleSupervisory 47,500 MaleMid-level 68,000 MaleExecutive122,550 FemaleSupervisory 45,300 FemaleMid-level 61,000.

Slides:



Advertisements
Similar presentations
Selecting a Data Analysis Technique: The First Steps
Advertisements

Multiple Regression. Multiple regression Typically, we want to use more than a single predictor (independent variable) to make predictions Regression.
USING DUMMY VARIABLES IN REGRESSION MODELS. Qualitative Variables Qualitative variables can be introduced into regression models using dummy variables.
 Will help you gain knowledge in: ◦ Improving performance characteristics ◦ Reducing costs ◦ Understand regression analysis ◦ Understand relationships.
Example T1: T2: Constraint: Susan’s salary = Jane’s salary
The Regression Equation  A predicted value on the DV in the bi-variate case is found with the following formula: Ŷ = a + B (X1)
Slide 1 Incorporating Nonmetric Data with Dummy Variables For many of the multivariate techniques we will study, it is assumed that the independent or.
General Qualitative Data, and “Dummy Variables” How might we have represented “make-of-car” in the motorpool case, had there been more than just two makes?
Multiple Linear Regression Introduction. Multiple Regression One continuous Y, two or more X variables. X variables may be continuous or dichotomous k.
Objective 17 Word Problems: Evaluate algebraic expressions ©2002 by R. Villar All Rights Reserved.
Developing and Using a Simple Regression Equation. The simple regression model is based on the equation for a straight line: Yc = A+BX.
Statistics 350 Lecture 16. Today Last Day: Introduction to Multiple Linear Regression Model Today: More Chapter 6.
1 Qualitative Independent Variables Sometimes called Dummy Variables.
Introduction to Logistic Regression. Simple linear regression Table 1 Age and systolic blood pressure (SBP) among 33 adult women.
1 SOC 3811 Basic Social Statistics. 2 Reminder  Hand in your Assignment 4.
Faculty Salary Equity Study University of North Carolina at Chapel Hill Faculty Council Report November 1, 2002.
Empirical methods take real-world data estimate size of relationship between variables two types  regression analysis  natural experiments take real-world.
DATA ANALYSIS III MKT525. Multiple Regression Simple regression:DV = a + bIV Multiple regression: DV = a + b 1 IV 1 + b 2 IV 2 + …b n IV n b i = weight.
Statistical Analysis of the Two Group Post-Only Randomized Experiment.
APIM with Between- and Within Dyads Outcomes David A. Kenny December 11, 2014.
Class 20: Thurs., Nov. 18 Specially Constructed Explanatory Variables –Dummy variables for categorical variables –Interactions involving dummy variables.
Sociology 601 Class 26: December 1, 2009 (partial) Review –curvilinear regression results –cubic polynomial Interaction effects –example: earnings on married.
Beyond Bivariate: Exploring Multivariate Analysis.
Copyright © 2014, 2011 Pearson Education, Inc. 1 Chapter 25 Categorical Explanatory Variables.
1 MF-852 Financial Econometrics Lecture 9 Dummy Variables, Functional Form, Trends, and Tests for Structural Change Roy J. Epstein Fall 2003.
Correlation and Regression. The test you choose depends on level of measurement: IndependentDependentTest DichotomousContinuous Independent Samples t-test.
CHAPTER 9 DUMMY VARIABLE REGRESSION MODELS
Women in WW 2. Rosie the Riveter 1.Listen to the Rosie the Riveter Song on laptop number 1.
Bell Ringer In a factory of 200 people, 120 are men who earn $18 an hour. The women earn $20 an hour. What is the average salary of the employees at the.
Evaluating Algebraic Expressions, given values
When and why to use Logistic Regression?  The response variable has to be binary or ordinal.  Predictors can be continuous, discrete, or combinations.
Web example squares-means-marginal-means-vs.html.
1 Applied biostatistics Francisco Javier Barón López Dpto. Medicina Preventiva Universidad de Málaga – España
Copyright © 2006 The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Dummy Variable Regression Models chapter ten.
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.
Multiple Regression  Similar to simple regression, but with more than one independent variable R 2 has same interpretation R 2 has same interpretation.
Take advantage of the Maths Study Centre CB Open 11am – 5pm Semester Weekdays for help. Check out some regression videos.
SW388R7 Data Analysis & Computers II Slide 1 Incorporating Nonmetric Data with Dummy Variables The logic of dummy-coding Dummy-coding in SPSS.
Class 5 Multiple Regression Models. We can readily imagine that there may be several factors that we can include in our model to explain test scores.
Developing a Hiring System Measuring Applicant Qualifications or Statistics Can Be Your Friend!
“GLMrous designs” “GLMrous designs” “Are you regressed or something?” “Pseudonyms & aliases” “Pseudonyms & aliases” Models I Models II.
The Analysis of Covariance ANACOVA. Multiple Regression 1.Dependent variable Y (continuous) 2.Continuous independent variables X 1, X 2, …, X p The continuous.
Lecture 1 Outline: Thu, Sep 4 Introduction/Syllabus Course outline Some useful guidelines Case studies and
The General Linear Model. Estimation -- The General Linear Model Formula for a straight line y = b 0 + b 1 x x y.
Beginners statistics Assoc Prof Terry Haines. 5 simple steps 1.Understand the type of measurement you are dealing with 2.Understand the type of question.
Ethnic Identity Kim & Gelfand Learning Check What is the independent variable? What are the dependent variables? What is the moderating variable? What.
Causality, Null Hypothesis Testing, and Bivariate Analysis
Curvilinear Relationships
STT : Intro. to Statistical Learning
Chapter 11 Analysis of Covariance
Multivariate Analysis
The Correlation Coefficient (r)
Learning Objectives For models with dichotomous intendant variables, you will learn: Basic terminology from ANOVA framework How to identify main effects,
Statistical Analysis of the Randomized Block Design
Multiple Regression Example
Least Squares ANOVA & ANCOV
Graduates Total Debt by Gender, 2015, 2016, and 2017
Graduates Total Debt by Gender, 2015 and 2016
Logistic Regression --> used to describe the relationship between
Session 4.1: We Approach the End
Questionnaires Jerry Post Copyright © 1997
Section 5 Multiple Regression.
Generalized Linear Mixed Models
Average Work Week of Persons Who Usually Work Full-time (1995) Rones et al., (April 1997) Hours of work since the mid-70s. Monthly Labor Review, p. 7 Men.
Chengyuan Yin School of mathematics
Cases. Simple Regression Linear Multiple Regression.
Chapter 14 Multiple Regression
The difference between Equal Pay and Gender Pay
Risk (aOR) of breast cancer associated with the combination of putative at-risk genotypes of the MRN genes and the reproductive risk factors of a history.
Business Intent Presentation
Presentation transcript:

ANOVA GenderManagement Level Average Salary MaleSupervisory 47,500 MaleMid-level 68,000 MaleExecutive122,550 FemaleSupervisory 45,300 FemaleMid-level 61,000 FemaleExecutive 89,500

Dummy Variable Regression Y = a + b1x1 +b2x2 where y is salary, x1 is years of experience, and x2 is the dummy variable (men or women) Salary Years of experience Salary Years of experience Men Women Men Women

Logistic Regression Y = a + b1x1 + b2x2… where y is a dichotomous variable such as employee promoted or not promoted, and the Xs are continuous variables Y X Y X