- Email: [email protected]
Statistical and computational methods in data analysis
Computer Physics Communications 16 (1979) 281 © North-Holland Publishing Company
BOOK REVIEW Statistical and Computational Methods in Data Analysis Siegmund Brandt, North-Holland, 1976. $ 35.95/Dfl. 90.00
Many books on statistics fall into two categories: elernentaiy texts, which explain in detail the basic methods most commonly used, and which illustrate those methods by a variety of numerical examples for which the computation can be done by hand; more advanced texts, where the emphasis is on the development of the theory rather than on its direct applications. By contrast, the present book, which is the second edition of a book first published in 1970, is written for users of mathematical statistics who wish to employ standard methods to handle a substantial amount of data. It therefore goes beyonds the elementary texts while retaining the motivation of application. This is not a book from which to learn statistics as such. The fundamental concepts are presented in a compact form, and at times the reader will need to fill in substantial parts of the mathematical argument. Neither will the reader obtain any real sense of statistical thinking. As a preparation for this book, the reader should have studied one of the elementary texts, in the course of which he will have met and performed many routine exercises which fix in his mind the basic methods and the circumstances under which they may be applied. The reader will also require a good working knowledge of linear algebra and advanced calculus (including the calculus of functions of several variables), With this background, the reader will be able to proceed to a study of this book. The opening chapters present in an elegant way the main statistical conceptswhich lead on to a discussion of the important distributions, ideas of sampling and the familiar tests for statistical hypotheses. A number of short examples are provided. However, the main feature of this book is the provision of FORTRAN
programs and routines for many of the processes discussed. Their value is that they are presented in context, Some of the routines are essentially ifiustrative of how programs can be written based on mathematical methods. For example, the routine to generate pseudo-random numbers should not be transcribed into the reader’s own programs (which point the author himself makes): instead, the package probably available at the reader’s computer installation should be used. Indeed, a similar comment could be applied to several of the routines given in the book. The advantages of the author’s presentation are that the routines give insight into the sort of processes programmed in “black-box” packages, that the routines are based directly on the mathematics in the book, and the way in which routines (and therefore packages) can be used as building blocks in the construction of programs. Applications in the book include general least squares fitting, the analysis of variance, linear regression and the analysis of time series. The book is based on a series of lectures given to physics students and particle physicists, although readers in other scientific fields would find the book useful. Lecture courses in this area tend to be tailored to suit the needs of the students involved and the interests of the lecturer. While the book may not always be suitable as a course book, its contents and style would be of value to a lecturer preparing such a course and it would serve at least as a consultation text for the students. It would also serve as an ideas book for lecturers giving more elementary courses in statistics. A. HIBBERT
281
BOOK REVIEW Statistical and Computational Methods in Data Analysis Siegmund Brandt, North-Holland, 1976. $ 35.95/Dfl. 90.00
Many books on statistics fall into two categories: elernentaiy texts, which explain in detail the basic methods most commonly used, and which illustrate those methods by a variety of numerical examples for which the computation can be done by hand; more advanced texts, where the emphasis is on the development of the theory rather than on its direct applications. By contrast, the present book, which is the second edition of a book first published in 1970, is written for users of mathematical statistics who wish to employ standard methods to handle a substantial amount of data. It therefore goes beyonds the elementary texts while retaining the motivation of application. This is not a book from which to learn statistics as such. The fundamental concepts are presented in a compact form, and at times the reader will need to fill in substantial parts of the mathematical argument. Neither will the reader obtain any real sense of statistical thinking. As a preparation for this book, the reader should have studied one of the elementary texts, in the course of which he will have met and performed many routine exercises which fix in his mind the basic methods and the circumstances under which they may be applied. The reader will also require a good working knowledge of linear algebra and advanced calculus (including the calculus of functions of several variables), With this background, the reader will be able to proceed to a study of this book. The opening chapters present in an elegant way the main statistical conceptswhich lead on to a discussion of the important distributions, ideas of sampling and the familiar tests for statistical hypotheses. A number of short examples are provided. However, the main feature of this book is the provision of FORTRAN
programs and routines for many of the processes discussed. Their value is that they are presented in context, Some of the routines are essentially ifiustrative of how programs can be written based on mathematical methods. For example, the routine to generate pseudo-random numbers should not be transcribed into the reader’s own programs (which point the author himself makes): instead, the package probably available at the reader’s computer installation should be used. Indeed, a similar comment could be applied to several of the routines given in the book. The advantages of the author’s presentation are that the routines give insight into the sort of processes programmed in “black-box” packages, that the routines are based directly on the mathematics in the book, and the way in which routines (and therefore packages) can be used as building blocks in the construction of programs. Applications in the book include general least squares fitting, the analysis of variance, linear regression and the analysis of time series. The book is based on a series of lectures given to physics students and particle physicists, although readers in other scientific fields would find the book useful. Lecture courses in this area tend to be tailored to suit the needs of the students involved and the interests of the lecturer. While the book may not always be suitable as a course book, its contents and style would be of value to a lecturer preparing such a course and it would serve at least as a consultation text for the students. It would also serve as an ideas book for lecturers giving more elementary courses in statistics. A. HIBBERT
281