IVOR GRATTAN-GUINNESS
The Norton History of the Mathematical Sciences
A comprehensive and authoritative account of the development of the subject that lies at the heart of so many sciences.
Beginning with the Babylonian and Egyptian mathematicians of antiquity, Ivor Grattan-Guinness charts the growth of mathematics through its refinement by ancient Greeks and then medieval Arabs, to its systematic development by Europeans from the Middle Ages to the early twentieth century. The book describes the evolution of arithmetic and geometry, trigonometry and algebra, the interplay between mathematics, physics, and mathematical astronomy, and "new" branches such as probability and statistics, "succeeding masterfully in
viewing the history of mathematics from a new perspective."*
"Grattan-Guinness has achieved a synthesis here of remarkable historical and mathematical scope and sensitivity." - *Professor Karen Hunger Parshall, editor of Historia Mathematica
Ivor Grattan-Guinness is professor of the history of mathematics and logic at Middlesex University, England.
December / ISBN 0-393-04650-8 / 832 pages / MATHEMATICS
Brian S. Everitt,
Graham Dunn
Applied Multivariate Data Analysis
Description:
Emphasising the exploratory use of data analysis, this up-to-date text is aimed at readers already familiar with elementary statistics.
After an introduction to data types and the statistical knowledge required to use the book, the authors go on to provide detailed explorations of multivariate data, regression models and latent variable models. Modern techniques such as multidimensional scaling, cluster analysis, generalised linear models and structural equation models are included.
Key Features:
* Stresses the importance of searching for patterns in data
* Interprets the output from statistical software
* Includes many real-data worked examples and end of chapter exercises
Contents:
Approaches to Analysing Data: Data and statistics; Mathematical and statistical background
Exploring Multivariate Data: The initial examination of multivariate data; Reducing the dimensionality
of multivariate data - principal components and correspondence analysis; Multi-dimensional scaling;
Cluster analysis
Regression Models: The generalised linear model; Regression and the analysis of variance; Linear
models for categorical data; Models for rates and survival times; Analysis of repeated measures;
Discriminant analysis
Latent Variable Models: Factor analysis; Covariance structure models
References
Appendix A - Programs and packages; Appendix B - Answers to selected exercises
Index.
Readership: Statistics undergraduates and researchers in the social and behavioural sciences.
Simon French , School of Computer Studies, University of Leeds, UK,
David R. Insua , Universidad Rey Juan Carlos, Madrid, Spain
Statistical Decision Theory
Kendall's Library of Statistics 9
Description:
Statistical decision theory provides a framework on which many statistical procedures may be built and justified. However, it is not exclusively a subdicipline of statistics, but provides models for academic disciplines ranging from history and economics to psychology, political science and operational research. Statisticians need to understand the mathematics and techniques of decision analysis in order that their inferential methods may interface with the other parts of the analysis. This new addition to 'Kendall's Library of Statistics' presents a broad overview of decision theory and its applications before focusing on statistical decision theory.
Key Features:
* Presents a broad picture of the applications of decision theory
* Examines developments in decision theory
Contents:
Normative, descriptive and prescriptive approaches to decision analysis
Axiomatic bases of subjective probability, value and utility models
Multi-attribute models
Statistical decision theory
Minimax theory
Bayesian statistical decision theory
Robustness and sensitivity analysis
Design problems
Sequential models.
Readership: Graduate statisticians.
Allan Donner , Department of Epidemiology and Biostatistics, University of Western Ontario, Canada,
Neil S. Klar , Department of Biostatistics, Dana-Faber Cancer Research Institute, Harvard, Boston, USA
Design and Analysis of Cluster Randomisation Trials
In Health Research
Description:
It is becoming increasingly common for health researchers to randomise intact social units, or 'clusters', such as families, medical practices or entire communities in clinical trials. The purpose of this text is to present methods that are appropriate to cluster randomisation design, as well as to discuss related issues such as ethics and informed consent. The history of cluster randomisation trials is also discussed and placed in the larger context of the history of clinical trials in general.
Key Features:
* Internationally respected authors
* No other books on this subject
* Important topic in medical statistics
Contents:
Experimental design of cluster randomisation trials
Analysis of data from cluster randomisation trials
Reporting the results of a cluster randomisation trial
Meta-analysis of cluster randomisation trials
Appendix I: Review of available software
Appendix II: Data sets used in examples.
Readership:
Medical researchers, including medical statisticians, epidemiologists, behavioural scientists, pharmaceutical researchers.
John Bowers , Formerly Department of Mathematics, University of Leeds, UK
Matrices and Quadratic Forms
Modular Mathematics Series
Description:
As a basic of equation and problem-solving, linear algebra is the most widely taught sub-division of
pure mathematics. Building on both skills and knowledge attained during A-level and earlier degree
modules in linear algebra, "Matrices and Quadratic Forms" provides a lively and accessible
introduction to matrices and quadratic forms. Examples and exercises are used as teaching aids
throughout and ideas for investigation and project work help to place the subject in context. Student
motivation and learning is encouraged by the inclusion of historical contexts, real-life situations and
the discussion of links with other areas of mathematics. Providing a concise and thorough
introduction to matrices and quadratic forms, this is an essential purchase for all students of
mathematics.
Key Features:
* Ideal follow-on-text to Allenby's 'Linear Algebra'
* Concise and affordable
* Written in a straightforward and accessible style
* A key topic in maths and sciences
Contents:
Questions about matrices and quadratic forms
Partitioned matrices
Vector spaces
Linear transformations
The matrix representation of linear transformations
Similar matrices
Diagonalisable matrices
The Cayley-Hamilton theorem
The minimum polynomial
Euclidean vector spaces /Orthogonal matrices
Quadratic forms
Positive definite quadratic forms
Further developments.
Readership:
Undergraduates in mathematics studying a 2nd course in linear algebra. Also undergraduate
scientists and engineers.
Binding: Paperback
Dimensions: 244 x 172mm
Published: 24/06/2000
ISBN: 0340691387
G. Janacek , Senior Lecturer, University of East Anglia, UK
Practical Time Series
Arnold Texts in Statistics
Description:
A time series is a set of ordered observations of an individual or event taken at different points in time.
The essential quality of time series is that observations are measured against time, as distinct from
those which are not ordered at all (e.g. a random sample taken simultaneously). The study of time-ordered data is an important branch of statistics with applications as variable as medicine, engineering and economics.
Janacek's book is a practical hands-on look at how the analysis works, rather than a mere discussion
of theory. A disk accompanying the book contains data which students can use in their analysis.
Key Features:
* Support material on Arnold website containing macros written in various public domain statistical software
* Offers a practical rather than a theoretical approach
* Author is a leading researcher in the field
Readership:
Senior undergraduates in statistics, mathematics and economics. Postgraduate researchers in engineering and medicine.