Regression Using Jmp

Regression Using Jmp

by Ph.D. Rudolf J. Freund, Ramon C. Littell, Lee Creighton
     
 

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)

Table of Contents

Acknowledgmentsv
Using This Bookvii
1Regression Concepts1
1.1What Is Regression?1
1.2Statistical Background13
1.3Terminology and Notation15
1.4Regression with JMP23
2Regressions in JMP25
2.1Introduction25
2.2A Model with One Independent Variable27
2.3A Model with Several Independent Variables32
2.4Additional Results from Fit Model36
2.5Further Examination of Model Parameters50
2.6Plotting Observations54
2.7Predicting to a Different Set of Data63
2.8Exact Collinearity: Linear Dependency67
2.9Summary70
3Observations73
3.1Introduction73
3.2Outlier Detection74
3.3Specification Errors91
3.4Heterogeneous Variances96
3.5Summary104
4Collinearity: Detection and Remedial Measures105
4.1Introduction105
4.2Detecting Collinearity107
4.3Model Restructuring115
4.4Variable Selection125
4.5Summary136
5Polynomial and Smoothing Models139
5.1Introduction139
5.2Polynomial Models with One Independent Variable140
5.3Polynomial Models with Several Variables151
5.4Response Surface Plots156
5.5A Three-Factor Response Surface Experiment158
5.6Smoothing Data167
5.7Summary173
6Special Applications of Linear Models175
6.1Introduction175
6.2Errors in Both Variables176
6.3Multiplicative Models181
6.4Spline Models191
6.5Indicator Variables195
6.6Binary Response Variable: Logistic Regression203
6.7Summary214
7Nonlinear Models215
7.1Introduction215
7.2Estimating the Exponential Decay Model216
7.3Fitting a Growth Curve with the Nonlinear Platform229
7.4Summary236
8Regression with JMP Scripting Language237
8.1Introduction237
8.2Performing a Simple Regression237
8.3Regression Matrices241
8.4Collinearity Diagnostics242
8.5Summary246
References247
Index249

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