G89.2229 Multiple Regression Week 11 Wednesday

Aug 20, 2012

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G89.2229 Multiple Regression Week 11 Wednesday

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1. G89.2229 Lect 11W Logistic Regression Review Maximum Likelihood Estimates Probit Regression and Example Model Fit G89.2229 Multiple Regression Week 11 (Wednesday)

2. G89.2229 Lect 11W Logistic Regression Review Additive probability models are often not ideal. p is bounded by [0,1]. Log odds of p [w(p)] has nice properties. Unbounded, with w(p)=0 for p=.5. Important equations

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g89.2229 multiple regression week 14 monday

G89.2229 Lect 14M. Overview of Course. For some outcome Y and explanatory variables X'=[X1 X2 ... Xp],We considered E(Y | X)To be modeled descriptivelyAs a structural modelAdditive models for E(Y | X)Main effects linear modelsAdditive polynomial modelsLinear models with interactions. G89.2229 Lect 14M.

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Chapter 11 Multiple Linear Regression

Chapter 11 Multiple Linear Regression. Group Project AMS 572. Group Members. Yuanhua Li. From Left to Right: Yongjun Cheng, William Ho, Katy Sharpe, Renyuan Luo, Farahnaz Maroof, Shuqiang Wang, Cai Rong, Jianping Zhang, Lingling Wu. . Overview. 1-3 Multiple Linear Regression --William Ho

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G89.2229 Multiple Regression Week 7 Wednesday

G89.2229 Lect 7W. Representing Interaction in the Regression Equation. If we believe that the effect of X1 varies as a function of level of a second variable, X2, we can build a simple multiplicative interactive effect.Y=b0 b1X1 b2X2 b3(X1*X2) eThis multiplicative term creates a curved surface in

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G89.2229 Multiple Regression Week 4 Monday

G89.2229 Lect 4M. Suppression. Sometimes the semipartial effect for X1 (i.e. b1) in Y = b0 b1X1 b2X2 e is larger in absolute magnitude than the bivariate effect in Y = b0 b1X1 eThis has been called suppressionExample:X1 is stressY is distressX2 is copingClassic pattern is when one of

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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

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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

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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

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G89.2229 Multiple Regression Week 3 Wednesday

G89.2229 Lect 3W. The geometry of E(Y|X1 X2) = b0 b1X1 b2X2. E(Y|X1 X2)= b0 b1X1 b2X2 describes a plane.For every change in either X1 or X2, E(Y|X1 X2) changes linearlyThe distribution of X1 and X2 does not affect the form of the plane itself, but it does affect the estimates of b0, b1, an

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G89.2229 Multiple Regression Week 6 Wednesday

G89.2229 Lect 6W. Constructing polynomial fits. Two approaches for constructing polynomial fitsSimply create squared, cubed versions of XCenter first: Create squared, cubed versions of (X-C)Xc=(X-`X)Xc and Xc2 will have little or no correlationBoth approach yield identical fitsCentered polynom

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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 + 

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G89.2229 Multiple Regression Week 11 Monday

G89.2229 Lect 11M. Binary outcomes in psychology. Psychologists tend to study quantitative processes such as reaction time, strength of attitudes, achievement scores, and severity of depressionIn some cases the outcome is binary or categoricalDecisions (e.g. to sit next to certain person)Passing

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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

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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

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Lecture 11: Multiple Regression. February 19 th , 2014. Question. Assuming there is no curve on the exam, how do you think you scored? (it was out of 100 excluding the bonus) 90 or higher 80-89 70-79 60-69 59 or lower. Administrative. Exam 1 Results soon… Homework 5 Due Monday

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STAT E-150: Week 11 Multiple Logistic Regression. By Kela Roberts. Section Overview. Questions HW Questions Review Key Concepts from Logistic Regression Sample Questions Additional Questions. Homework Review. Wald statistic – checks each individual term in the logistic model

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11. Multiple Regression and Correlation. y – response variable x 1 , x 2 , … , x k -- a set of explanatory variables In this chapter, all variables are assumed to be quantitative . Chapters 12-14 show how to incorporate categorical variables also in a regression model.

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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 :.

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11. Multiple Regression. y – response variable x 1 , x 2 , … , x k -- a set of explanatory variables In this chapter, all variables assumed to be quantitative . Chapters 12-14 show how to incorporate categorical variables also in a regression model.

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11. Multiple Regression

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