Practical Data Analysis with JMP, Third Edition, 3rd Edition

Chapter 16: Residuals Analysis and Estimation

Overview

Conditions for Least Squares Estimation

Residuals Analysis

Linearity

Curvature

Influential Observations

Normality

Constant Variance

Independence

Estimation

Confidence Intervals for Parameters

Confidence Intervals for Y|X

Prediction Intervals for Y|X

Application

Overview

Regression analysis is a powerful and flexible technique that we can reliably apply under certain conditions. Given a data table with continuous X-Y pairs, we can always use the ordinary least squares (OLS) method to fit a line and generate results like those we saw in Chapter 15. However, if we want to draw general conclusions or use an estimated regression equation to estimate, predict, or project Y values, it is critical ...