ISBN: 0-471-97033-6
Hardcover
324 pages
July 2006
An applied and concise treatment of statistical regression
techniques for business students and professionals who have
little or no background in calculus
Regression analysis is an invaluable statistical methodology in
business settings and is vital to model the relationship between
a response variable and one or more predictor variables, as well
as the prediction of a response value given values of the
predictors. In view of the inherent uncertainty of business
processes, such as the volatility of consumer spending and the
presence of market uncertainty, business professionals use
regression analysis to make informed decisions. Applied
Regression Modeling: A Business Approach offers a practical,
workable introduction to regression analysis for upper-level
undergraduate business students, MBA students, and business
managers, including auditors, financial analysts, retailers,
economists, production managers, and professionals in
manufacturing firms.
The book's overall approach is strongly based on an abundant use
of illustrations and graphics and uses major statistical software
packages, including SPSSR, MinitabR, SASR, and R/S-PLUSR.
Detailed instructions for use of these packages, as well as for
Microsoft Office ExcelR, are provided, although Excel does not
have a built-in capability to carry out all the techniques
discussed.
Applied Regression Modeling: A Business Approach offers special
user features, including:
A companion Web site with all the datasets used in the book,
classroom presentation slides for instructors, additional
problems and ideas for organizing class time around the material
in the book, and supplementary instructions for popular
statistical software packages. An Instructor's Solutions Manual
is also available.
A generous selection of problems?many requiring computer work?in
each chapter with fullyworked-out solutions
Two real-life dataset applications used repeatedly in examples
throughout the book to familiarize the reader with these
applications and the techniques they illustrate
A chapter containing two extended case studies to show the direct
applicability of the material
A chapter on modeling extensions illustrating more advanced
regression techniques through the use of real-life examples and
covering topics not normally seen in a textbook of this nature
More than 100 figures to aid understanding of the material
Applied Regression Modeling: A Business Approach fully prepares
professionals and students to apply statistical methods in their
decision-making, using primarily regression analysis and modeling.
To help readers understand, analyze, and interpret business data
and make informed decisions in uncertain settings, many of the
examples and problems use real-life data with a business focus,
such as production costs, sales figures, stock prices, economic
indicators, and salaries. A calculus background is not required
to understand and apply the methods in the book.
ISBN: 0-470-04295-8
Hardcover
336 pages
July 2006
A hands-on introduction to the tools needed for rigorous and
theoretical mathematical reasoning
Successfully addressing the frustration many students experience
as they make the transition from computational mathematics to
advanced calculus and algebraic structures, Theorems,
Corollaries, Lemmas, and Methods of Proof equips students with
the tools needed to succeed while providing a firm foundation in
the axiomatic structure of modern mathematics.
This essential book:
Clearly explains the relationship between definitions,
conjectures, theorems, corollaries, lemmas, and proofs
Reinforces the foundations of calculus and algebra
Explores how to use both a direct and indirect proof to prove a
theorem
Presents the basic properties of real numbers
Discusses how to use mathematical induction to prove a theorem
Identifies the different types of theorems
Explains how to write a clear and understandable proof
Covers the basic structure of modern mathematics and the key
components of modern mathematics
A complete chapter is dedicated to the different methods of proof
such as forward direct proofs, proof by contrapositive, proof by
contradiction, mathematical induction, and existence proofs. In
addition, the author has supplied many clear and detailed
algorithms that outline these proofs.
Theorems, Corollaries, Lemmas, and Methods of Proof uniquely
introduces scratch work as an indispensable part of the proof
process, encouraging students to use scratch work and creative
thinking as the first steps in their attempt to prove a theorem.
Once their scratch work successfully demonstrates the truth of
the theorem, the proof can be written in a clear and concise
fashion. The basic structure of modern mathematics is discussed,
and each of the key components of modern mathematics is defined.
Numerous exercises are included in each chapter, covering a wide
range of topics with varied levels of difficulty.
Intended as a main text for mathematics courses such as Methods
of Proof, Transitions to Advanced Mathematics, and Foundations of
Mathematics, the book may also be used as a supplementary
textbook in junior- and senior-level courses on advanced
calculus, real analysis, and modern algebra.
1. Auflage - September 2006
2006. 500 Seiten, Hardcover
- Handbuch/Nachschlagewerk -
ISBN 3-527-40623-9 - Wiley-VCH, Berlin
This handbook provides an up-to-date survey of current research
topics and applications of Time Series Analysis written by
leading experts in their fields.
Contents
1 Overview: Handbook of time series analysis
(B. Schelter, M. Winterhalder, and J. Timmer)
2 Nonlinear Analysis of Time Series Data
(Henry D. I. Abarbanel and Ulrich Parlitz)
3 Local and Cluster Weighted Modeling for Time Series Prediction
(David Engster and Ulrich Parlitz)
4 Deterministic and probabilistic forecasting in reconstructed
state spaces
(Holger Kantz and Eckehard Olbrich)
5 Dealing with randomness in biosignals
(Patrick Celka, Rolf Vetter, Elly Gysels, and Trevor Hine)
6 Robust detail-preserving signal extraction
(Ursula Gather, Roland Fried, and Vivian Lanius)
7 Coupled oscillators approach in analysis of bivariate data
(Michael Rosenblum, Laura Cimponeriu, and Arkady Pikovsky)
8 Nonlinear dynamical models from chaotic time series: methods
and applications
(D.A. Smirnov and B.P. Bezruchko)
9 Data-driven analysis of nonstationary brain signals
(Mario Chavez, Claude Adam, Stefano Boccaletti, and Jacques
Martinerie)
10 Synchronization Analysis and Recurrence in Complex Systems
(M. C. Romano, M. Thiel, J. Kurths, M. Rolfs, R. Engbert, and R.
Kliegl)
11 Detecting coupling in the presence of noise and nonlinearity
(Theoden I. Netoff, Thomas L. Carroll, Louis M. Pecora, and
Steven J. Schiff)
12 Linear models for mutivariate time series
(Manfred Deistler)
13 Spatio-TemporalModeling for Biosurveillance Using a Spatially
Constrained State Space Model
(David S. Stoffer and Myron J. Katzoff)
14 Graphical modeling of dynamic relationships in multivariate
time series
(Michael Eichler)
15 Multivariate Signal Analysis by ParametricModels
(K. J. Blinowska and M. Kaminski)
16 Computer Intensive Testing for the Influence between Time
Series
(Luiz A. Baccala, Daniel Y. Takahashi, and Koichi Sameshima)
17 Granger Causality: Basic Theory and Application to
Neuroscience
(Mingzhou Ding, Yonghong Chen, and Steven L. Bressler)
18 Granger Causality on Spatial Manifolds: applications to
Neuroimaging
(P. A. Valdes-Sosa, J.M. Bornot-Sanchez, M. Vega-Hernandez, L.
Melie-Garcia, A. Lage-Castellanos, and E. Canales-Rodriguez)
ISBN: 0-470-85502-9
Hardcover
376 pages
October 2006
Description
Risk assessment and risk analysis are now firmly fixed in the
biostatistician's and engineer's lexicon. Reliability is the
other key element in the mix for smooth running projects and
operations. In the modern industrial era, economic factors have
resulted in the construction and operation of larger and more
complex process plant. Engineers are working to maximize the
benefits of modern processing technology while reducing the
safety risks to acceptable levels. However, each processing plant
has unique problems and each must be individually assessed to
identify, evaluate and control associated hazards. Statistical
methods play a key role in the quantification of reliability, and
since the advent of MCMC, Bayesian methods have become
increasingly important. This book addresses the need for a sound
introduction to the mathematical and statistical aspects of
reliability analysis from a Bayesian perspective. It features
many real examples, taken from the authorfs vast experience,
and lots of applications from reliability engineering. The author
is well respected in both the statistical/Bayesian and
reliability communities.
ISBN: 0-471-68755-3
Hardcover
352 pages
September 2006
Description
This book provides a broad foundation for topology, including
general, geometric, differential, combinatorial and algebraic
topology. It is designed for use either for a first course in
topology beginning in chapter one, a first course in geometric
topology beginning in chapter three or a course in applied
topology. The book is structured around the theoretical framework
of topology, and abundant illustrations and applications provide
intuition and put the subject in modern setting. Topics include
open sets, compactness, homotopy, surface classification, index
theory on surfaces, manifolds and complexes, topological groups,
the fundamental group and homology. Modern applications of
topology have played an important role solving a diverse spectrum
of applied problems. In this text serious attention is given to
recent applications of topology in computer graphics, economics,
dynamical systems, condensed matter physics, biology, robotics,
chemistry, cosmology, material science, computational topology,
population modeling and other areas of science and engineering.
Most applications are presented in optional sections, allowing an
instructor to customize the presentation.
ISBN: 0-471-73507-8
Hardcover
480 pages
November 2006
Description
Updated with new material, this Fifth Edition of the most widely
used book in combinatorial problems explains how to reason and
model combinatorically. It also stresses the systematic analysis
of different possibilities, exploration of the logical structure
of a problem, and ingenuity. Combinatorical reasoning underlies
all analysis of computer systems. It plays a similar role in
discrete operations research problems and in finite probability.
This book seeks to develop proficiency in basic discrete math
problem solving in the way that a calculus text develops
proficiency in basic analysis problem solving.