Fitting Equations to Data

Fitting Equations to Data
Author: Cuthbert Daniel
Publisher: Wiley-Interscience
Total Pages: 490
Release: 1980
Genre: Mathematics
ISBN:

Helps any serious data analyst with a computer to recognize the strengths and limitations of data, to test the assumptions implicit in the least squares methods used to fit the data, to select appropriate forms of the variables, to judge which combinations of variables are most influential, and to state the conditions under which the fitted equations are applicable. This edition includes numerous extensions and new devices such as component and component-plus-residual plots, cross verification with a second sample, and an index of required x-precision; also, the search for better subset equations is enlarged to cover 262,144 alternatives. The methods described have been applied in agricultural, environmental, management, marketing, medical, physical, and social sciences. Mathematics is kept to the level of college algebra.

Applied Statistics

Applied Statistics
Author: Lothar Sachs
Publisher: Springer Science & Business Media
Total Pages: 737
Release: 2012-12-06
Genre: Mathematics
ISBN: 1461252466

This outline of statistics as an aid in decision making will introduce a reader with limited mathematical background to the most important modern statistical methods. This is a revised and enlarged version, with major extensions and additions, of my "Angewandte Statistik" (5th ed.), which has proved useful for research workers and for consulting statisticians. Applied statistics is at the same time a collection of applicable statistical methods and the application of these methods to measured and/or counted observations. Abstract mathematical concepts and derivations are avoided. Special emphasis is placed on the basic principles of statistical formulation, and on the explanation of the conditions under which a certain formula or a certain test is valid. Preference is given to consideration of the analysis of small sized samples and of distribution-free methods. As a text and reference this book is written for non-mathematicians, in particular for technicians, engineers, executives, students, physicians as well as researchers in other disciplines. It gives any mathematician interested in the practical uses of statistics a general account of the subject. Practical application is the main theme; thus an essential part of the book consists in the 440 fully worked-out numerical examples, some of which are very simple; the 57 exercises with solutions; a number of different compu tational aids; and an extensive bibliography and a very detailed index. In particular, a collection of 232 mathematical and mathematical-statistical tables serves to enable and to simplify the computations.

Experimental Design: A Chemometric Approach

Experimental Design: A Chemometric Approach
Author: S.N. Deming
Publisher: Elsevier
Total Pages: 455
Release: 1993-06-04
Genre: Science
ISBN: 008086838X

Now available is the second edition of a book which has been described as "...an exceptionally lucid, easy-to-read presentation... would be an excellent addition to the collection of every analytical chemist. I recommend it with great enthusiasm." (Analytical Chemistry)N.R. Draper reviewed the first edition in Publication of the International Statistical Institute "...discussion is careful, sensible, amicable, and modern and can be recommended for the intended readership."The scope of the first edition has been revised, enlarged and expanded. Approximately 30% of the text is new. The book first introduces the reader to the fundamentals of experimental design. Systems theory, response surface concepts, and basic statistics serve as a basis for the further development of matrix least squares and hypothesis testing. The effects of different experimental designs and different models on the variance-covariance matrix and on the analysis of variance (ANOVA) are extensively discussed. Applications and advanced topics (such as confidence bands, rotatability, and confounding) complete the text. Numerous worked examples are presented.The clear and practical approach adopted by the authors makes the book applicable to a wide audience. It will appeal particularly to those with a practical need (scientists, engineers, managers, research workers) who have completed their formal education but who still need to know efficient ways of carrying out experiments. It will also be an ideal text for advanced undergraduate and graduate students following courses in chemometrics, data acquisition and treatment, and design of experiments.

Multivariate Statistical Modeling in Engineering and Management

Multivariate Statistical Modeling in Engineering and Management
Author: Jhareswar Maiti
Publisher: CRC Press
Total Pages: 637
Release: 2022-10-25
Genre: Mathematics
ISBN: 1000618390

The book focuses on problem solving for practitioners and model building for academicians under multivariate situations. This book helps readers in understanding the issues, such as knowing variability, extracting patterns, building relationships, and making objective decisions. A large number of multivariate statistical models are covered in the book. The readers will learn how a practical problem can be converted to a statistical problem and how the statistical solution can be interpreted as a practical solution. Key features: Links data generation process with statistical distributions in multivariate domain Provides step by step procedure for estimating parameters of developed models Provides blueprint for data driven decision making Includes practical examples and case studies relevant for intended audiences The book will help everyone involved in data driven problem solving, modeling and decision making.

Fitting Equations to Data

Fitting Equations to Data
Author: Cuthbert Daniel
Publisher: Wiley-Interscience
Total Pages: 486
Release: 1999-08-30
Genre: Mathematics
ISBN:

Helps any serious data analyst with a computer to recognize the strengths and limitations of data, to test the assumptions implicit in the least squares methods used to fit the data, to select appropriate forms of the variables, to judge which combinations of variables are most influential, and to state the conditions under which the fitted equations are applicable. This edition includes numerous extensions and new devices such as component and component-plus-residual plots, cross verification with a second sample, and an index of required x-precision; also, the search for better subset equations is enlarged to cover 262,144 alternatives. The methods described have been applied in agricultural, environmental, management, marketing, medical, physical, and social sciences. Mathematics is kept to the level of college algebra.

Principal Component Analysis

Principal Component Analysis
Author: I.T. Jolliffe
Publisher: Springer Science & Business Media
Total Pages: 283
Release: 2013-03-09
Genre: Mathematics
ISBN: 1475719043

Principal component analysis is probably the oldest and best known of the It was first introduced by Pearson (1901), techniques ofmultivariate analysis. and developed independently by Hotelling (1933). Like many multivariate methods, it was not widely used until the advent of electronic computers, but it is now weIl entrenched in virtually every statistical computer package. The central idea of principal component analysis is to reduce the dimen sionality of a data set in which there are a large number of interrelated variables, while retaining as much as possible of the variation present in the data set. This reduction is achieved by transforming to a new set of variables, the principal components, which are uncorrelated, and which are ordered so that the first few retain most of the variation present in all of the original variables. Computation of the principal components reduces to the solution of an eigenvalue-eigenvector problem for a positive-semidefinite symmetrie matrix. Thus, the definition and computation of principal components are straightforward but, as will be seen, this apparently simple technique has a wide variety of different applications, as weIl as a number of different deri vations. Any feelings that principal component analysis is a narrow subject should soon be dispelled by the present book; indeed some quite broad topics which are related to principal component analysis receive no more than a brief mention in the final two chapters.

Multivariate Methods in Aquaculture Research

Multivariate Methods in Aquaculture Research
Author: Mark Prein
Publisher: WorldFish
Total Pages: 222
Release: 1993
Genre: Aquaculture
ISBN: 9711022850

Papers in this book discuss the emphasis on tilapia, modelling of pond aquaculture, exploratory and confirmatory methods for multivariate analysis, the relationships between multiple regression and analysis of variance and the rationale for sharing and analyzing "old", public domain data, all with emphasis on tropical/subtropical aquaculture systems.