Multiple Regression - Assessing Collinearity

Jun 24, 2013

290 likes | 647 Views

Multiple Regression. Recall we perform multiple regression when we have several explanatory variables we want to relate to a continuous response Y.How the explanatory variables relate to each other is fundamental to understanding their relationship to the response variable.Relationships between the explanatory variables are called collinearity. This can be mild, or severe..

Share Presentation

Embed Code

Link

serenity

Download Presentation

Multiple Regression - Assessing Collinearity

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

Multiple Regression

Multiple Regression. In multiple regression, we consider the response , y, to be a function of more than one predictor variable, x1, …, xk Easiest to express in terms of matrices. Multiple Regression. Let Y be a col vector whose rows are the observations of the response Let X be a matrix.

1.11k views • 45 slides

Multiple Regression

Multiple Regression. Multiple regression is an extension of bivariate regression to include more than one independent variable.Limitations of bivariate regression: - often simplistic - biased estimates if relevant predictors are omitted - lack of fit does not show that X is unrelated to Y. McGra

938 views • 64 slides

Multiple Regression

What is Multiple Regression? . y = b0 b1x1 b2x2.... bkxk.? One dependent variableSeveral independent variables x1,x2..xk. Assumptions. Quantitative dataIndependent observationEach value of IV, the distribution of the DV must be normalThe variance of the distribution of the DV should be co

709 views • 39 slides

Multiple Regression

Multiple Regression. Dr. Andy Field. Aims. Understand When To Use Multiple Regression . Understand the multiple regression equation and what the betas represent. Understand Different Methods of Regression Hierarchical Stepwise Forced Entry

2.35k views • 51 slides

Multiple Regression

Multiple Regression . Control of Confounding Variables. Randomization Matching Adjustment Direct Indirect Mantel-Haenszel Multiple Regression Linear Logistic Poisson Cox. Stratified methods . Limitations of the Stratified Methods.

822 views • 61 slides

MULTIPLE REGRESSION

MULTIPLE REGRESSION. OVERVIEW. What Makes it Multiple? Additional Assumptions Methods of Entering Variables Adjusted R 2 Using z-Scores Categorical Predictors. WHAT MAKES IT MULTIPLE?. Predict from a combination of two or more predictor (X) variables

475 views • 32 slides

Multiple Regression

Multiple Regression. W&W, Chapter 13, 15(3-4). Introduction. Multiple regression is an extension of bivariate regression to take into account more than one independent variable. The most simple multivariate model can be written as: Y =  0 +  1 X 1 +  2 X 2 + 

458 views • 19 slides

Multiple Regression

Multiple Regression. The Basics. Multiple Regression (MR). Predicting one DV from a set of predictors, the DV should be interval/ratio or at least assumed I/R if using Likert scale for instance. Assumptions. Y must be normally distributed (no skewness or outliers) X’s

439 views • 29 slides

Multiple Regression

Multiple Regression. Introduction. In this chapter we extend the simple linear regression model, and allow for any number of independent variables. We expect to build a model that fits the data better than the simple linear regression model. Introduction. We shall use computer printout to

680 views • 39 slides

Multiple Regression

STAT 101 Dr. Kari Lock Morgan 11/27/12. Multiple Regression. SECTION 10.3 Categorical variables Variable selection Confounding variables revisited. US States.

586 views • 45 slides

MULTIPLE REGRESSION

MULTIPLE REGRESSION. OVERVIEW. What Makes it Multiple? Additional Assumptions Methods of Entering Variables Adjusted R 2 Using z-Scores. WHAT MAKES IT MULTIPLE?. Predict from a combination of two or more predictor (X) variables.

440 views • 28 slides

Multiple Regression

Multiple Regression. SPH 247 Statistical Analysis of Laboratory Data. Cystic Fibrosis Data. Cystic fibrosis lung function data lung function data for cystic fibrosis patients (7-23 years old) age a numeric vector. Age in years. sex a numeric vector code. 0: male, 1:female.

293 views • 18 slides

Multiple regression

Multiple regression. An example. Theory. Optimism causes political participation. General optimism Specific optimism. Dependent variable. polpartbes _ | count | Freq. Percent Cum. ------------+-----------------------------------

294 views • 16 slides

Assessing Studies Based on Multiple Regression

Assessing Studies Based on Multiple Regression. Internal and External Validity Threats to Internal Validity Omitted Variable Bias Errors-in Variables Sample Selection Simultaneous Causality Application to Test Score Data. Is there a systematic way to assess regression studies?.

535 views • 43 slides

Multiple Regression

Multiple Regression. Using Additional Explanatory Variables. Try this …. Look at the relationship between average driving distance & scoring average for 146 LPGA golfers in 2009. The least-squares regression line is ŷ = 87.95 – 0.0609x. Interpret the slope :.

209 views • 8 slides

Multiple Regression

Multiple Regression. Chapter 18. 18.1 Introduction. In this chapter we extend the simple linear regression model, and allow for any number of independent variables. We expect to build a model that fits the data better than the simple linear regression model.

639 views • 45 slides

Multiple Regression

Multiple Regression. Chapter 17. Introduction. In this chapter we extend the simple linear regression model, and allow for any number of independent variables. We expect to build a model that fits the data better than the simple linear regression model. Introduction.

764 views • 51 slides

Multiple regression:

Multiple regression:. Yi = B 0 + B 1 X 1 +B 2 X 2 + B 3 X 3 +e where each of the Betas estimate the effect of one independent variable. This allows the regression to "control" for each of the other factors simultaneously

148 views • 5 slides

Multiple Regression

Multiple Regression. Multiple Regression. The test you choose depends on level of measurement: Independent Variable Dependent Variable Test Dichotomous Continuous Independent Samples t-test Dichotomous Nominal Nominal Cross Tabs Dichotomous Dichotomous

866 views • 69 slides

Multiple Regression

Multiple Regression. Class 22. Multiple Regression (MR). Y = b o + b 1 + b 2 + b 3 + ……b x + ε. Multiple regression (MR) can incorporate any number of predictors in model. “Regression plane” with 2 predictors, after that it becomes increasingly difficult to visualize result.

622 views • 35 slides

Multiple regression

Multiple regression. Regression. Problem: to draw a straight line through the points that best explains the variance. Regression. Problem: to draw a straight line through the points that best explains the variance. Regression.

326 views • 31 slides

Multiple Regression

University of Warwick, Department of Sociology, 2014/15 SO 201: SSAASS (Surveys and Statistics) (Richard Lampard ) Week 6 Regression: ‘Loose Ends’. Multiple Regression. GHQ = (-0.47 x INCOME) + (-1.95 x HOUSING) + 5.74 For B = 0.47, t = -1.51 (& p = 0.139 > 0.05)

304 views • 27 slides

More Related