Advanced Regression Models with SAS and R

✍ Scribed by Olga Korosteleva

• Publisher: Chapman and Hall/CRC
• Year: 2018
• Tongue: English
• Leaves: 305
• Edition: 1
• Category: Library

✦ Synopsis

Advanced Regression Models with SAS and R exposes the reader to the modern world of regression analysis. The material covered by this book consists of regression models that go beyond linear regression, including models for right-skewed, categorical and hierarchical observations. The book presents the theory as well as fully worked-out numerical examples with complete SAS and R codes for each regression. The emphasis is on model accuracy and the interpretation of results. For each regression, the fitted model is presented along with interpretation of estimated regression coefficients and prediction of response for given values of predictors.

Features:

• Presents the theoretical framework for each regression.
• Discusses data that are categorical, count, proportions, right-skewed, longitudinal and hierarchical.
• Uses examples based on real-life consulting projects.
• Provides complete SAS and R codes for each example.
• Includes several exercises for every regression.

Advanced Regression Models with SAS and R is designed as a text for an upper division undergraduate or a graduate course in regression analysis. Prior exposure to the two software packages is desired but not required.

The Author:

Olga Korosteleva is a Professor of Statistics at California State University, Long Beach. She teaches a large variety of statistical courses to undergraduate and master's students. She has published three statistical textbooks. For a number of years, she has held the position of faculty director of the statistical consulting group. Her research interests lie mostly in applications of statistical methodology through collaboration with her clients in health sciences, nursing, kinesiology, and other fields.

✦ Table of Contents

Contents
Illustrations
Tables
Preface
Notation
1 Introduction
1.1 Historical Background
1.2 Devices That Illustrate Principles of Analytical Dynamics
1.3 Scope of This Book
2 Mathematical Preliminaries
2.1 Linear Systems
2.2 Differential Geometry
2.3 Optimization
2.4 Exercises
3 Kinematics of Discrete Systems
3.1 Spherical Kinematics
3.2 Spatial Kinematics
3.3 Kinematic Chains
3.4 Kinematic Constraints and Degrees of Freedom
3.5 Exercises
4 Conservation Principles
4.1 The Newton-Euler Principle
4.2 Exercises
5 Zeroth-Order Variational Principles
5.1 Virtual Displacements
5.2 D’Alembert’s Principle of Virtual Work
5.3 Hamilton’s Principle of Least Action
5.4 Canonical Hamiltonian Formulation
5.5 Elimination of Multipliers
5.6 Exercises
6 First-Order Variational Principles
6.1 Virtual Velocities
6.2 Jourdain’s Principle of Virtual Power
6.3 Kane’s Formulation
6.4 Exercises
7 Second-Order Variational Principles
7.1 Virtual Accelerations
7.2 Gauss’s Principle
7.3 Gauss’s Principle of Least Constraint
7.4 Gibbs-Appell Formulation
7.5 Exercises
8 Dynamics in Task Space
8.1 Task Space Framework
8.2 Constrained Dynamics in Task Space
8.3 Exercises
9 Applications to Biomechanical Systems
9.1 Musculoskeletal and Neuromuscular Dynamics
9.2 Constrained Dynamics of Biomechanical Systems
10 Software for Analytical Dynamics
10.1 General Purpose Mathematical Software
10.2 Dedicated Multibody Dynamics Software
Appendix Inclusion of Flexible Bodies
A.1 Continuum Kinematics
A.2 Continuum Dynamics
A.3 Subsystem Assembly
References
Index