Download Free Bmdp Biomedical Computer Programs P Series Book in PDF and EPUB Free Download. You can read online Bmdp Biomedical Computer Programs P Series and write the review.

BMDP-79

University of California, Los Angeles. Health Sciences Computing Facility

Published: 1979

Total Pages: 900


Get eBook

An attempt is made in this book to give scientists a detailed working knowledge of the powerful mathematical tools available to aid in data interpretation, especially when con fronted with large data sets incorporating many parameters. A minimal amount of com puter knowledge is necessary for successful applications, and we have tried conscien tiously to provide this in the appropriate sections and references. Scientific data are now being produced at rates not believed possible ten years ago. A major goal in any sci entific investigation should be to obtain a critical evaluation of the data generated in a set of experiments in order to extract whatever useful scientific information may be present. Very often, the large number of measurements present in the data set does not make this an easy task. The goals of this book are thus fourfold. The first is to create a useful reference on the applications of these statistical pattern recognition methods to the sciences. The majority of our discussions center around the fields of chemistry, geology, environmen tal sciences, physics, and the biological and medical sciences. In Chapter IV a section is devoted to each of these fields. Since the applications of pattern recognition tech niques are essentially unlimited, restricted only by the outer limitations of.
"First published in Japanese in 2010 by University of Tokyo Press, as Human ecology of the tropical forest: life traditional culture and natural environment of Gidra."--T.p. verso.
Geared toward upper-level undergraduates and graduate students, this two-part treatment deals with the foundations of multivariate analysis as well as related models and applications. Starting with a look at practical elements of matrix theory, the text proceeds to discussions of continuous multivariate distributions, the normal distribution, and Bayesian inference; multivariate large sample distributions and approximations; the Wishart and other continuous multivariate distributions; and basic multivariate statistics in the normal distribution. The second half of the text moves from defining the basics to explaining models. Topics include regression and the analysis of variance; principal components; factor analysis and latent structure analysis; canonical correlations; stable portfolio analysis; classifications and discrimination models; control in the multivariate linear model; and structuring multivariate populations, with particular focus on multidimensional scaling and clustering. In addition to its value to professional statisticians, this volume may also prove helpful to teachers and researchers in those areas of behavioral and social sciences where multivariate statistics is heavily applied. This new edition features an appendix of answers to the exercises.
Back in print at a good price. To see the many websites referencing this book, in Google enter "cluster analysis" (in quotes) and Romesburg. Headlines of 5-star reviews on Amazon.com: "A very clear 'how to' book on cluster analysis" (C. Fielitz, Bristol, TN); "An excellent introduction to cluster analysis" (T. W. Powell, Shreveport, LA). A recent (2004) review in Journal of Classification (21:279-283) says: "We should be grateful to the author for his insistence in bringing forth important issues, which have not got yet that level of attention they deserve. I wish this journal could devote more efforts in promoting the scientific inquiry and discussions of methodology of clustering in scientific research [as Cluster Analysis for Researchers does]." To see or search inside the book, go to www.google.com, type in the book's title, and click on it when it comes up (or copy and paste in your browser's window the following URL: http://print.google.com/print?isbn=1411606175 ).