Chapter 11--Regression and Correlation Methods

# In r we use the command anovafullreduced chapter 11

This preview shows page 1. Sign up to view the full content.

This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: t of Regression Coeﬃcients Prediction (Forecasting) Dummy Variables An Example: For the determinants of house price example, we are interested in testing if age of the house, number of bathrooms and lot size have any additional predictive value. That is, H0 : βAge = βBath = βLot = 0. The following ANOVA was obtained for the reduced model, Source Regression Residual Total SS 64367.055 15102.787 79469.843 df 2 48 50 MS 32183.528 314.641 F 102.286 Test the hypothesis and make conclusions. In R, we use the command anova(full,reduced). Chapter 11: Regression and Correlation Methods Stat 491: Biostatistics Sig. 0.000 Introduction Least Square Estimates of the Parameters Inference about the Parameters Prediction Assessing Adequacy of Fit Correlation Multiple Regression Introduction Inferences in Multiple Regression Tests for Subset of Regression Coeﬃcients Prediction (Forecasting) Dummy Variables Partial Correlation Coeﬃcient The partial correlation between y and xj ,denoted by rxj y ·x1 ,x2 ,...,xj −1 ,xj +1 ,...,xk , is the correlation between y and xj after the linear eﬀects of the other x s is taken out of both y and xj . It is a measure of the strength of linear relation between y and xj after eliminating (adjusting for) their linear association with the other x s. It is calculated as the correlation between ey ·x1 ,x2 ,...,xj −1 ,xj +1 ,...,xk and exj ·x1 ,x2 ,...,xj −1 ,xj +1 ,...,xk where ey ·x1 ,x2 ,...,xj −1 ,xj +1 ,...,xk = y − µy |x1 ,x2 ,...,xj −1 ,xj +1 ,...,xk . ˆ Similar relation exists between the slope and the partial slope. Chapter 11: Regression and Correlation Methods Stat 491: Biostatistics Introduction Least Square Estimates of the Parameters Inference about the Parameters Prediction Assessing Adequacy of Fit Correlation Multiple Regression Introduction Inferences in Multiple Regression Tests for Subset of Regression Coeﬃcients Prediction (Forecasting) Dummy Variables Expected Value of y and Individual Value of y Let xn+...
View Full Document

## This note was uploaded on 02/03/2014 for the course STAT 491 taught by Professor Solomonharrar during the Fall '12 term at Montana.

Ask a homework question - tutors are online