Synopses & Reviews
This text provides a thorough introduction to the statistical methods used in the experimental sciences and to the numerical methods used to implement them. Bridging the gap between statistical theory and practical problems, the treatment emphasizes concise but rigorous mathematics while retaining its focus on applications.After introducing probability and random variables, the book turns to the generation of random numbers (and application to Monte Carlo methods) and to important distributions (such as binomial, Poisson, and normal distributions). Subsequent chapters discuss statistical samples, the maximum likelihood method, and the testing of statistical hypothesis. The text concludes with a detailed discussion of several important statistical methods: least squares, minimization, analysis of variance, polynomial regression, and analysis of time series. Appendices present the necessary methods of matrix algebra and combinatorics as well as many useful formulae, algorithms, and computer routines. The reader is presumed to have a sound basic knowledge of differential and integral calculus and some knowledge of vectors and matrices.The richly illustrated text is intended for graduate students setting out on experimental research, but it should also provide a useful reference and programming guide for scientists and professionals. To guide the student, the text includes many worked-out examples as well as problems (many with hints or solutions). An accompanying cross-platform CD-ROM provides an extensive source program library (now in Java) to be used as a tool kit for the readers own applications, a graphics library (for Windows or Linux), and many sample programs (as source code and executable files).New in the Fourth Edition:- The chapter on statistical tests now also contains a section on tolerance limits, a concept of particular importance in industrial applications.- The chapter on least squares is extended. Special emphasis is given to examples with correlated measurement.- There are even more examples and problems with solutions then previously.- Included in the book is a CD-ROM containing two program libraries (data analysis and graphics) as well as many example programs and solutions to programming problems posed in the text. The programs are now presented in the platform independent and object oriented Java language. Many examples contain the widow-like user interfaces typical of Java. All programs are also given in Fortran and C as previously.
Review
The book is concise, but gives a sufficiently rigorous mathematical treatment of practical statistical methods for data analysis. The content of the book is built up very clearly. The different subjects are nicely illustrated and the computer programs are a real support for the better understanding of the subjects treated. The book is both a textbook and a book of reference. It can be of great use to all who are involved with data analysis. -PHYSICALIA
Review
Serves as a nice reference guide for any scientist interested in the fundamentals of data analysis on the computer. -THE AMERICAN STATISTICIAN"
Synopsis
1. 1 Typical Problems of Data Analysis Every branch of experimental science, after passing through an early stage of qualitative description, concerns itself with quantitative studies of the phe- nomena of interest, i. e., measurements. In addition to designing and carrying out the experiment, an importal1t task is the accurate evaluation and complete exploitation of the data obtained. Let us list a few typical problems. 1. A study is made of the weight of laboratory animals under the influence of various drugs. After the application of drug A to 25 animals, an average increase of 5 % is observed. Drug B, used on 10 animals, yields a 3 % increase. Is drug A more effective? The averages 5 % and 3 % give practically no answer to this question, since the lower value may have been caused by a single animal that lost weight for some unrelated reason. One must therefore study the distribution of individual weights and their spread around the average value. Moreover, one has to decide whether the number of test animals used will enable one to differentiate with a certain accuracy between the effects of the two drugs. 2. In experiments on crystal growth it is essential to maintain exactly the ratios of the different components. From a total of 500 crystals, a sample of 20 is selected and analyzed.
Synopsis
Bridging the gap between statistical theory and physical experiment, this is a thorough introduction to the statistical methods used in the experimental physical sciences and to the numerical methods used to implement them. The treatment emphasises concise but rigorous mathematics but always retains its focus on applications. Readers are assumed to have a sound basic knowledge of differential and integral calculus and some knowledge of vectors and matrices. After an introduction to probability, random variables, computer generation of random numbers and important distributions, the book turns to statistical samples, the maximum likelihood method, and the testing of statistical hypotheses. The discussion concludes with several important statistical methods: least squares, analysis of variance, polynomial regression, and analysis of time series. Appendices provide the necessary methods of matrix algebra, combinatorics, and many sets of useful algorithms and formulae.
Synopsis
This richly illustrated reference provides a thorough introduction to the statistical methods used in the experimental sciences and to the numerical methods used to implement them. The accompanying CD-ROM provides an extensive program source library to be used as a toolkit for the reader's own applications, graphics library, and many sample programs. 273 illus.
Table of Contents
1. Introduction; 2. Probabilities; 3. Random Variables; 4. Computer- Generated Random Numbers: The Monte Carlo Method; 5. Some Important Distributions and Theorems; 6. Samples; 7. The Method of Maximum Likelihood; 8. Testing Statistical Hypotheses; 9. The Method of Least Squares; 10. Function Minimization; 11. Analysis of variance; 12. Linear and Polynomial Regression; 13. Time-Series Analysis; Appendix A: Matrix Calculation; Appendix B: Combinatorics; Appendix C: Formulas and Programs for Statistical Functions; Appendix D: The Gamma Function and Related Functions. methods and Programs for Their Computation; Appendix E: Utility Programs; Appendix F: The Graphics Programming Package GRPACK; Appendix G: Software Installation and technical Hints; Appendix H: Collection of Formulas; Appendix I: Statistical Tables; Literature; List of Computer Programs; Register.