Regression Using JMP

✍ Scribed by Rudolf J. Freund; Ramon C. Littell; Lee Creighton

• Publisher: SAS Publishing
• Year: 2003
• Tongue: English
• Leaves: 283
• Edition: 1
• Category: Library

✦ Synopsis

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, the authors 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.

✦ Table of Contents

Contents......Page 6
Prerequisites......Page 10
Organization......Page 11
Conventions......Page 12
Conventions for Output......Page 13
Seeing Regression......Page 14
A Physical Model of Regression......Page 20
Correlation......Page 22
Seeing Correlation......Page 24
Statistical Background......Page 26
Terminology and Notation......Page 28
Partitioning the Sums of Squares......Page 29
Hypothesis Testing......Page 31
Using the Generalized Inverse......Page 34
Regression with JMP......Page 36
Introduction......Page 38
A Model with One Independent Variable......Page 40
A Model with Several Independent Variables......Page 45
Additional Results from Fit Model......Page 49
Predicted Values and Confidence Intervals......Page 50
Sequential and Partial: Two Types of Sums of Squares......Page 54
Standardized Coefficients......Page 56
Regression through the Origin......Page 57
Tests for Subsets and Linear Functions of Parameters......Page 63
Changing the Model......Page 66
Plotting Observations......Page 67
Interactive Methods......Page 68
Regression with Deleted Observations......Page 71
Deleting Effects......Page 73
Predicting to a Different Set of Data......Page 76
Exact Collinearity: Linear Dependency......Page 80
Summary......Page 83
Introduction......Page 86
Outlier Detection......Page 87
Univariate and Bivariate Investigations......Page 89
Residuals and Studentized Residuals......Page 93
Influence Statistics......Page 98
Specification Errors......Page 104
Heterogeneous Variances......Page 109
Weighted Least Squares......Page 113
Summary......Page 117
Introduction......Page 118
Detecting Collinearity......Page 120
Leverage Plots......Page 123
Analysis of Structure: Principal Compo-nents, Eigenvalues, and Eigenvectors......Page 124
Model Restructuring......Page 128
Redefining Variables......Page 129
Multivariate Structure: Principal Compo-nent Regression......Page 130
Variable Selection......Page 138
All Possible Models......Page 139
Choosing Useful Models......Page 142
Selection Procedures......Page 145
Stepwise Probabilities......Page 146
Summary......Page 149
Introduction......Page 152
Polynomial Models with One Independent Variable......Page 153
Polynomial Centering......Page 154
Analysis Using Fit Y by X......Page 158
Analysis Using Fit Model......Page 161
Polynomial Models with Several Variables......Page 164
Response Surface Plots......Page 169
A Three-Factor Response Surface Experiment......Page 171
The Moving Average......Page 180
The Time Series Platform......Page 182
Smoothing Splines......Page 185
Summary......Page 186
Introduction......Page 188
Errors in Both Variables......Page 189
Multiplicative Models......Page 194
Spline Models......Page 204
Indicator Variables......Page 208
Binary Response Variable: Logistic Regression......Page 216
Summary......Page 227
Introduction......Page 228
Estimating the Exponential Decay Model......Page 229
Seeing the Sum of Squares Surface......Page 236
Estimates and Standard Errors......Page 237
Fitting a Growth Curve with the Nonlinear Platform......Page 242
Summary......Page 249
Performing a Simple Regression......Page 250
Regression Matrices......Page 254
Collinearity Diagnostics......Page 255
Summary......Page 259
Index......Page 262

✦ Subjects

Библиотека;Компьютерная литература;SAS / JMP;