# Regression Using Jmp

ISBN-10: 1590471601

ISBN-13: 9781590471609

Pub. Date: 06/11/2003

Publisher: SAS Institute Inc.

Filled with examples, Regression Using JMP introduces you to the basics of regression analysis using JMP software. You will learn how to perform regression analyses using a wide variety of models, including linear and nonlinear models. Taking a tutorial approach, authors Rudolf Freund, Ramon Littell, and Lee Creighton cover the customary Fit Y by X and Fit Model

## Overview

Filled with examples, Regression Using JMP introduces you to the basics of regression analysis using JMP software. You will learn how to perform regression analyses using a wide variety of models, including linear and nonlinear models. Taking a tutorial approach, authors Rudolf Freund, Ramon Littell, and Lee Creighton cover the customary Fit Y by X and Fit Model platforms, as well as the new features and capabilities of JMP Version 5. Output is covered in helpful detail. Thorough discussion of the following is also presented: confidence limits, examples using JMP scripting language, polynomial and smoothing models, regression in the context of linear model methodology, and diagnosis of and remedies for data problems including outliers and collinearity. Statistical consultants familiar with regression analysis and basic JMP concepts will appreciate the conversational, "what to look for" and "what if" scenarios presented. Non-statisticians with a working knowledge of statistical concepts will learn how to use JMP successfully for data analysis.

## Product Details

ISBN-13:
9781590471609
Publisher:
SAS Institute Inc.
Publication date:
06/11/2003
Edition description:
New Edition
Pages:
288
Product dimensions:
7.50(w) x 9.25(h) x 0.60(d)

## Related Subjects

 Acknowledgments v Using This Book vii 1 Regression Concepts 1 1.1 What Is Regression? 1 1.2 Statistical Background 13 1.3 Terminology and Notation 15 1.4 Regression with JMP 23 2 Regressions in JMP 25 2.1 Introduction 25 2.2 A Model with One Independent Variable 27 2.3 A Model with Several Independent Variables 32 2.4 Additional Results from Fit Model 36 2.5 Further Examination of Model Parameters 50 2.6 Plotting Observations 54 2.7 Predicting to a Different Set of Data 63 2.8 Exact Collinearity: Linear Dependency 67 2.9 Summary 70 3 Observations 73 3.1 Introduction 73 3.2 Outlier Detection 74 3.3 Specification Errors 91 3.4 Heterogeneous Variances 96 3.5 Summary 104 4 Collinearity: Detection and Remedial Measures 105 4.1 Introduction 105 4.2 Detecting Collinearity 107 4.3 Model Restructuring 115 4.4 Variable Selection 125 4.5 Summary 136 5 Polynomial and Smoothing Models 139 5.1 Introduction 139 5.2 Polynomial Models with One Independent Variable 140 5.3 Polynomial Models with Several Variables 151 5.4 Response Surface Plots 156 5.5 A Three-Factor Response Surface Experiment 158 5.6 Smoothing Data 167 5.7 Summary 173 6 Special Applications of Linear Models 175 6.1 Introduction 175 6.2 Errors in Both Variables 176 6.3 Multiplicative Models 181 6.4 Spline Models 191 6.5 Indicator Variables 195 6.6 Binary Response Variable: Logistic Regression 203 6.7 Summary 214 7 Nonlinear Models 215 7.1 Introduction 215 7.2 Estimating the Exponential Decay Model 216 7.3 Fitting a Growth Curve with the Nonlinear Platform 229 7.4 Summary 236 8 Regression with JMP Scripting Language 237 8.1 Introduction 237 8.2 Performing a Simple Regression 237 8.3 Regression Matrices 241 8.4 Collinearity Diagnostics 242 8.5 Summary 246 References 247 Index 249

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