Regression Using JMP

Name: Regression Using JMP
Availability: OutOfStock
ISBN: 9780471483076

ISBN-10: 0471483079

ISBN-13: 9780471483076

Edition: 2003

Authors: Rudolf J. Freund, Ramon C. Littell, Lee Creighton, SAS Institute Staff

List price: $95.00

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Description:

JMP software dynamically links statistics with graphics to interactively explore, understand, and visualize data. It is designed for those who need to discover relationships and outliers in their data, and provides a comprehensive set of statistical tools in one package. It is used by professionals in many industries including manufacturing, Six Sigma, chemicals and plastics, pharmaceuticals, semiconductors, research, and academia. Regression Using JMP makes it easier for JMP users to apply the software to their own data analysis problems. In this book, a wide variety of data is used to illustrate the basic kinds of regression models that can be analyzed with JMP.

Book details

List price: $95.00
Copyright year: 2003
Publisher: John Wiley & Sons, Incorporated
Publication date: 8/28/2003
Binding: Paperback
Pages: 288
Size: 7.50" wide x 9.00" long x 0.50" tall
Weight: 1.100
Language: English



Acknowledgments


Using This Book



Regression Concepts



What Is Regression?



Statistical Background



Terminology and Notation



Regression with JMP



Regressions in JMP



Introduction



A Model with One Independent Variable



A Model with Several Independent Variables



Additional Results from Fit Model



Further Examination of Model Parameters



Plotting Observations



Predicting to a Different Set of Data



Exact Collinearity: Linear Dependency



Summary



Observations



Introduction



Outlier Detection



Specification Errors



Heterogeneous Variances



Summary



Collinearity: Detection and Remedial Measures



Introduction



Detecting Collinearity



Model Restructuring



Variable Selection



Summary



Polynomial and Smoothing Models



Introduction



Polynomial Models with One Independent Variable



Polynomial Models with Several Variables



Response Surface Plots



A Three-Factor Response Surface Experiment



Smoothing Data



Summary



Special Applications of Linear Models



Introduction



Errors in Both Variables



Multiplicative Models



Spline Models



Indicator Variables



Binary Response Variable: Logistic Regression



Summary



Nonlinear Models



Introduction



Estimating the Exponential Decay Model



Fitting a Growth Curve with the Nonlinear Platform



Summary



Regression with JMP Scripting Language



Introduction



Performing a Simple Regression



Regression Matrices



Collinearity Diagnostics



Summary


References


Index