SAS System for RegressionThird Edition or Optimal Control Systems

SAS System for Regression,Third Edition

Author: Rudolf Jakob Freund

Learn to perform a wide variety of regression analyses using SAS software with this example-driven revised favorite from SAS Publishing. With this third edition you will learn the basics of performing regression analyses using a wide variety of models including nonlinear models. Other topics include performing linear regression analyses using PROVC REG and diagnosing and providing remedies for data problems, including outliers and multicollinearity. Examples feature numerous SAS procedures including REG, PLOT, GPLOT, NLIN, RSREG, AUTOREG, PRINCOMP, and others. A helpful discussion of theory is supplied where necessary. Some knowledge of both regression and the SAS System are assumed. The updated third edition includes revisions, updated material, and new material. You'll find information on using SAS/INSIGHT software, regression with a binary response with emphasis on PROC LOGISTIC, and nonparametric regression (smoothing) using moving averages and PROC LOESS. Additionally, updated material throughout the book includes high-resolution PROC REG graphics output, data sets by the OUTEST option described and illustrated, and using PROC SCORE to predict another data set. Supports releases 6.12 and higher of SAS software.

Table of Contents:

	Acknowledgments	vii
Chapter 1	Regression Concepts	1
1.1	Statistical Background	1
1.2	Performing a Regression with the IML Procedure	9
1.3	Regression with the SAS System	12
Chapter 2	Using the REG Procedure	15
2.1	Introduction	15
2.2	A Model with One Independent Variable	17
2.3	A Model with Several Independent Variables	20
2.4	Various MODEL Statement Options	24
2.5	Further Examination of Model Parameters	36
2.6	Plotting Results	43
2.7	Creating Data I: the OUTPUT Statement	52
2.8	Creating Data II: Other Data Sets	55
2.9	Creating Data III: ODS Output	57
2.10	Predicting to a Different Set of Data	57
2.11	Exact Collinearity: Linear Dependency	60
2.12	Summary	62
Chapter 3	Observations	63
3.1	Introduction	63
3.2	Outlier Detection	64
3.3	Specification Errors	77
3.4	Heterogeneous Variances	81
3.5	Correlated Errors	86
3.6	Summary	94
Chapter 4	Multicollinearity: Detection and Remedial Measures	95
4.1	Introduction	95
4.2	Detecting Multicollinearity	97
4.3	Model Restructuring	101
4.4	Variable Selection	108
4.5	Biased Estimation	120
4.6	Summary	125
Chapter 5	Curve Fitting	127
5.1	Introduction	127
5.2	Polynomial Models with One Independent Variable	128
5.3	Polynomial Plots	132
5.4	Polynomial Models with Several Variables	134
5.5	Response Surface Plots	138
5.6	A Three-Factor Response Surface Experiment	142
5.7	Curve Fitting without a Model	148
5.8	Summary	155
Chapter 6	Special Applications of Linear Models	157
6.1	Introduction	157
6.2	Multiplicative Models	158
6.3	Spline Models	164
6.4	Indicator Variables	169
6.5	Binary Response Variable: Logistic Regression	173
6.6	Summary	184
Chapter 7	Nonlinear Models	185
7.1	Introduction	185
7.2	Estimating the Exponential Decay Model	186
7.3	Fitting a Growth Curve with the NLIN Procedure	192
7.4	Fitting Splines with Unknown Knots	198
7.5	Additional Comments on the NLIN Procedure	203
7.6	Summary	204
Chapter 8	Using SAS/INSIGHT Software for Regression	207
8.1	Introduction	207
8.2	Multiple Linear Regression: the BOQ Data	207
8.3	A Polynomial Response Surface: the FISH Data	215
8.4	Logistic Regression: the DYSTRO Data	218
8.5	Nonparametric Smoothing: the Barbados Data	221
8.6	Summary	225
	References	227
	Index	229

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Optimal Control Systems

Author: D Subbaram Naidu

The theory of optimal control systems has grown and flourished since the 1960's. Many texts, written on varying levels of sophistication, have been published on the subject. Yet even those purportedly designed for beginners in the field are often riddled with complex theorems, and many treatments fail to include topics that are essential to a thorough grounding in the various aspects of and approaches to optimal control.
Optimal Control Systems provides a comprehensive but accessible treatment of the subject with just the right degree of mathematical rigor to be complete but practical. It provides a solid bridge between "traditional" optimization using the calculus of variations and what is called "modern" optimal control. It also treats both continuous-time and discrete-time optimal control systems, giving students a firm grasp on both methods. Among this book's most outstanding features is a summary table that accompanies each topic or problem and includes a statement of the problem with a step-by-step solution. Students will also gain valuable experience in using industry-standard MATLAB and SIMULINK software, including the Control System and Symbolic Math Toolboxes.
Diverse applications across fields from power engineering to medicine make a foundation in optimal control systems an essential part of an engineer's background. This clear, streamlined presentation is ideal for a graduate level course on control systems and as a quick reference for working engineers.

Booknews

Naidu (Idaho State University) introduces the theory and concepts useful for optimizing the control of physical systems which are dynamic and hence described by ordinary differential equations. Written for a second level graduate course, the textbook covers the calculus of variations, the linear quadratic regulator system, discrete-time systems, the Pontryagin minimum principle, and constraints on control and state variables. MATLAB and SIMULINK implementations are provided. Annotation c. Book News, Inc., Portland, OR