Jamal Nazrul Islam

An Introduction to Mathematical Cosmology

Description
This book is a concise introduction to the mathematical aspects of the origin, structure and evolution of the universe. The book begins with a brief overview of observational cosmology and general relativity, and goes on to discuss Friedmann models, the Hubble constant, models with a cosmological constant, singularities, the early universe, inflation and quantum cosmology. This book is rounded off with a chapter on the distant future of the universe. The book is written as a textbook for advanced undergraduates and beginning graduate students. It will also be of interest to cosmologists, astrophysicists, astronomers, applied mathematicians and mathematical physicists.

Chapter Contents
Preface; 1. Some basic concepts and an overview of cosmology; 2. The Robertson-Walker metric and the Einstein equations; 3. The Friedmann models; 4. The Hubble constant and the deceleration parameter; 5. Models with a cosmological constant; 6. Singularities in cosmology; 7. The early universe; 8. The very early universe and inflation; 9. Quantum cosmology; 10. The distant future of the universe; 11. Some recent developments; References; Index.

ISBN: 0-521-49973-9
Binding: Paperback
Size: 230 x 154 mm
Pages: 202
Weight: 0.338kg

C. J. Pethick, H. Smith

Bose-Einstein Condensation in Dilute Gases

Description
In 1925 Einstein predicted that at low temperatures particles in a gas could all reside in the same quantum state. This gaseous state, a Bose-Einstein condensate, was produced in the laboratory for the first time in 1995 and investigating such condensates has become one of the most active areas in contemporary physics. The study of Bose-Einstein condensates in dilute gases encompasses a number of different subfields of physics, including atomic, condensed matter, and nuclear physics. The authors of this graduate-level textbook explain this exciting new subject in terms of basic physical principles, without assuming detailed knowledge of any of these subfields. Chapters cover the statistical physics of trapped gases, atomic properties, cooling and trapping atoms, interatomic interactions, structure of trapped condensates, collective modes, rotating condensates, superfluidity, interference phenomena, and trapped Fermi gases. Problem sets are also included in each chapter.

Chapter Contents
1. Introduction; 2. The non-interacting Bose gas; 3. Atomic properties; 4. Trapping and cooling of atoms; 5. Interactions between atoms; 6. Theory of the condensed state; 7. Dynamics of the condensate; 8. Microscopic theory of the Bose gas; 9. Rotating condensates; 10. Superfluidity; 11. Trapped clouds at non-zero temperature; 12. Mixtures and spinor condensates; 13. Interference and correlations; 14. Fermions; Appendix. Fundamental constants; Index.

ISBN: 0-521-66194-3
Binding: Hardback
ISBN: 0-521-66580-9
Binding: Paperback
Size: 255 x 179 mm
Pages: 414
Weight: 1.008kg
Figures: 36 line diagrams 1 half-tone

David W. Hosmer, Stanley Lemeshow

Applied Logistic Regression, 2nd Edition, Solutions Manual

ISBN: 0-471-20826-4
Paperback
280 Pages
September 2001

Supplements

From the reviews of the First Edition.

"An interesting, useful, and well-written book on logistic regression models . . . Hosmer and Lemeshow have used very little mathematics, have presented difficult concepts heuristically and through illustrative examples, and have included references."-Choice

"Well written, clearly organized, and comprehensive . . . the authors carefully walk the reader through the estimation of interpretation of coefficients from a wide variety of logistic regression models . . . their careful explication of the quantitative re-expression of coefficients from these various models is excellent."-Contemporary Sociology

"An extremely well-written book that will certainly prove an invaluable acquisition to the practicing statistician who finds other literature on analysis of discrete data hard to follow or heavily theoretical."-The Statistician

In this revised and updated edition of their popular book, David Hosmer and Stanley Lemeshow continue to provide an amazingly accessible introduction to the logistic regression model while incorporating advances of the last decade, including a variety of software packages for the analysis of data sets. Hosmer and Lemeshow extend the discussion from biostatistics and epidemiology to cutting-edge applications in data mining and machine learning, guiding readers step-by-step through the use of modeling techniques for dichotomous data in diverse fields. Ample new topics and expanded discussions of existing material are accompanied by a wealth of real-world examples-with extensive data sets available over the Internet.

Enrique Castillo, Antonio J. Conejo, Pablo Pedregal,
Ricardo Garcia, Natalia Alguacil

Building and Solving Mathematical Programming Models in Engineering and Science

ISBN: 0-471-15043-6
Hardcover
568 Pages
October 2001

Fundamental concepts of mathematical modeling
Modeling is one of the most effective, commonly used tools in engineering and the applied sciences. In this book, the authors deal with mathematical programming models both linear and nonlinear and across a wide range of practical applications.

Whereas other books concentrate on standard methods of analysis, the authors focus on the power of modeling methods for solving practical problems?clearly showing the connection between physical and mathematical realities?while also describing and exploring the main concepts and tools at work. This highly computational coverage includes:

Discussion and implementation of the GAMS programming system
Unique coverage of compatibility
Illustrative examples that showcase the connection between model and reality
Practical problems covering a wide range of scientific disciplines, as well as hundreds of examples and end-of-chapter exercises
Real-world applications to probability and statistics, electrical engineering, transportation systems, and more
Building and Solving Mathematical Programming Models in Engineering and Science is practically suited for use as a professional reference for mathematicians, engineers, and applied or industrial scientists, while also tutorial and illustrative enough for advanced students in mathematics or engineering.

Randall E. Schumacker and Allen Akers

UNDERSTANDING STATISTICAL CONCEPTS USING S-PLUS (paper w/disk)

ISBN: 0-8058-3623-3
Year: 2001

Binding paper w/disk
Page Count 368

Written as a supplemental text for an introductory or intermediate statistics course, this book is organized along the lines of many popular statistics texts. The chapters provide a good conceptual understanding of basic statistics and include exercises that use S-PLUS simulation programs. Each chapter lists a set of objectives and a summary. The book offers a rich insight into how probability has shaped statistical procedures in the behavioral sciences, as well as a brief history behind the creation of various statistics. Computational skills are kept to a minimum by including S-PLUS programs that run the exercises in the chapters. Students are not required to master the writing of S-PLUS programs, but explanations of how the programs work and program output are included in each chapter. S-PLUS is an advanced statistical package that has an extensive library of functions, which offer flexibility in writing customized routines. The S-PLUS functions provide the capability of programming object and dialog windows, which are commonly used in Windows software applications. The S-PLUS program also contains pull-down menus for the statistical analysis of data.

Contents: Preface. Part I: Introduction and Statistical Theory. Statistical Theory. Generating Random Numbers. Frequency Distributions. Stem and Leaf Plots. Population Distributions. Measures of Central Tendency. Measures of Dispersion. Sample Size Effects. Tchebysheff Inequality Theorem. Normal Bell-Shaped Curve. Part II: Probability and Probability Distributions. Probability. Joint Probability. Addition Law of Probability. Multiplication Law of Probability. Conditional Probability. Combinations and Permutations. Part III: Monte Carlo and Statistical Distributions. Binomial Distribution. Monte Carlo Simulation. Normal Distribution. t Distribution. Chi-square Distribution. F Distribution. Part IV: Sampling and Inference. Sampling Distributions. Central Limit Theorem. Confidence Intervals. Hypothesis Testing. Type I Error. Type II Error. Part V: Hypothesis Testing in Research. z Test Statistic for Proportions. Chi-square Test Statistic. t Test for Mean Differences. Analysis of Variance. Correlation. Linear Regression. Part VI: Replicability of Findings. Cross Validation. Jackknife. Bootstrap. Meta-Analysis. Significance Testing vs. Practical Importance. Appendix.