Linear Estimation and Design of Experiments
Author: D. D. Joshi
Publisher: New Age International
Published: 1987
Total Pages: 308
ISBN-13: 9780852265178
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Linear Estimation & Experimental Designs
Author: D. D. Joshi
Publisher: Wiley
Published: 1987-12-28
Total Pages: 288
ISBN-13: 9780470207406
DOWNLOAD EBOOKCovers the general mathematical theory of linear estimation and illustrates how the theory can be applied to the analysis of experimental data. Offers an integrated approach, placing proportionate emphasis on mathematical theory, its application to standard designs, and computational techniques. Provides the reader with a broad experience of the whole field, and instills the facility to apply the techniques to new designs as they are encountered. A special feature is the use throughout of R. C. Bose's approach to the theory of least squares, which completely avoids the use of side-conditions in the solution of normal equations. Chapters cover linear transformations and projections, multivariate normal and associated distributions, linear estimation, sums of squares, tests of linear hypotheses, the completely randomized design, incomplete block design: general theory, the general factorial experiment, the 3n factorial experiment, analysis of covariance, and more.
Design of Experiments
Author: Max Morris
Publisher: CRC Press
Published: 2010-07-27
Total Pages: 376
ISBN-13: 1439894906
DOWNLOAD EBOOKOffering deep insight into the connections between design choice and the resulting statistical analysis, Design of Experiments: An Introduction Based on Linear Models explores how experiments are designed using the language of linear statistical models. The book presents an organized framework for understanding the statistical aspects of experiment
A First Course in the Design of Experiments
Author: John H. Skillings
Publisher: Routledge
Published: 2018-05-08
Total Pages: 696
ISBN-13: 1351469975
DOWNLOAD EBOOKMost texts on experimental design fall into one of two distinct categories. There are theoretical works with few applications and minimal discussion on design, and there are methods books with limited or no discussion of the underlying theory. Furthermore, most of these tend to either treat the analysis of each design separately with little attempt to unify procedures, or they will integrate the analysis for the designs into one general technique. A First Course in the Design of Experiments: A Linear Models Approach stands apart. It presents theory and methods, emphasizes both the design selection for an experiment and the analysis of data, and integrates the analysis for the various designs with the general theory for linear models. The authors begin with a general introduction then lead students through the theoretical results, the various design models, and the analytical concepts that will enable them to analyze virtually any design. Rife with examples and exercises, the text also encourages using computers to analyze data. The authors use the SAS software package throughout the book, but also demonstrate how any regression program can be used for analysis. With its balanced presentation of theory, methods, and applications and its highly readable style, A First Course in the Design of Experiments proves ideal as a text for a beginning graduate or upper-level undergraduate course in the design and analysis of experiments.
Bayesian Estimation and Experimental Design in Linear Regression Models
Author: Jürgen Pilz
Publisher:
Published: 1991-07-09
Total Pages: 316
ISBN-13:
DOWNLOAD EBOOKPresents a clear treatment of the design and analysis of linear regression experiments in the presence of prior knowledge about the model parameters. Develops a unified approach to estimation and design; provides a Bayesian alternative to the least squares estimator; and indicates methods for the construction of optimal designs for the Bayes estimator. Material is also applicable to some well-known estimators using prior knowledge that is not available in the form of a prior distribution for the model parameters; such as mixed linear, minimax linear and ridge-type estimators.
A First Course in Linear Models and Design of Experiments
Author: N. R. Mohan Madhyastha
Publisher: Springer Nature
Published: 2020-11-13
Total Pages: 230
ISBN-13: 9811586594
DOWNLOAD EBOOKThis textbook presents the basic concepts of linear models, design and analysis of experiments. With the rigorous treatment of topics and provision of detailed proofs, this book aims at bridging the gap between basic and advanced topics of the subject. Initial chapters of the book explain linear estimation in linear models and testing of linear hypotheses, and the later chapters apply this theory to the analysis of specific models in designing statistical experiments. The book includes topics on the basic theory of linear models covering estimability, criteria for estimability, Gauss–Markov theorem, confidence interval estimation, linear hypotheses and likelihood ratio tests, the general theory of analysis of general block designs, complete and incomplete block designs, general row column designs with Latin square design and Youden square design as particular cases, symmetric factorial experiments, missing plot technique, analyses of covariance models, split plot and split block designs. Every chapter has examples to illustrate the theoretical results and exercises complementing the topics discussed. R codes are provided at the end of every chapter for at least one illustrative example from the chapter enabling readers to write similar codes for other examples and exercise.
Experimental Design and Data Analysis for Biologists
Author: Gerry P. Quinn
Publisher: Cambridge University Press
Published: 2002-03-21
Total Pages: 851
ISBN-13: 1139432893
DOWNLOAD EBOOKAn essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data. The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models. Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models. Multivariate techniques, including classification and ordination, are then introduced. Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results. The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature. The book is supported by a website that provides all data sets, questions for each chapter and links to software.
Applied Linear Statistical Models
Author: John Neter
Publisher: Irwin Professional Publishing
Published: 1974
Total Pages: 872
ISBN-13:
DOWNLOAD EBOOKSome basic results in probability and statistics. Basic regression analysis. General regression and correlation analysis. Basic analysis of variance. Multifactor analysis of variance. Experimental designs.
Handbook of Design and Analysis of Experiments
Author: Angela Dean
Publisher: CRC Press
Published: 2015-06-26
Total Pages: 946
ISBN-13: 146650434X
DOWNLOAD EBOOKThis carefully edited collection synthesizes the state of the art in the theory and applications of designed experiments and their analyses. It provides a detailed overview of the tools required for the optimal design of experiments and their analyses. The handbook covers many recent advances in the field, including designs for nonlinear models and algorithms applicable to a wide variety of design problems. It also explores the extensive use of experimental designs in marketing, the pharmaceutical industry, engineering and other areas.
Optimum Experimental Designs, With SAS
Author: Anthony Atkinson
Publisher: OUP Oxford
Published: 2007-05-24
Total Pages: 528
ISBN-13: 0191537942
DOWNLOAD EBOOKExperiments on patients, processes or plants all have random error, making statistical methods essential for their efficient design and analysis. This book presents the theory and methods of optimum experimental design, making them available through the use of SAS programs. Little previous statistical knowledge is assumed. The first part of the book stresses the importance of models in the analysis of data and introduces least squares fitting and simple optimum experimental designs. The second part presents a more detailed discussion of the general theory and of a wide variety of experiments. The book stresses the use of SAS to provide hands-on solutions for the construction of designs in both standard and non-standard situations. The mathematical theory of the designs is developed in parallel with their construction in SAS, so providing motivation for the development of the subject. Many chapters cover self-contained topics drawn from science, engineering and pharmaceutical investigations, such as response surface designs, blocking of experiments, designs for mixture experiments and for nonlinear and generalized linear models. Understanding is aided by the provision of "SAS tasks" after most chapters as well as by more traditional exercises and a fully supported website. The authors are leading experts in key fields and this book is ideal for statisticians and scientists in academia, research and the process and pharmaceutical industries.
