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In real applications most experimental situations are influenced by a large number of different factors. In these settings the design of an experiment leads to challenging optimization problems, even if the underlying relationship can be described by a linear model. Based on recent research, this book introduces the theory of optimum designs for complex models and develops general methods of reduction to marginal problems for large classes of models with relevant interaction structures.

This book provides the reader with user-friendly applications of normal distribution. In several variables it is called the multinormal distribution which is often handled using matrices for convenience. The author seeks to make the arguments less abstract and hence, starts with the univariate case and moves progressively toward the vector and matrix cases. The approach used in the book is a gradual one, going from one scalar variable to a vector variable and to a matrix variable. The author pre...

This book is an introduction to the principles and methodology of modern multivariate statistical analysis. It is written for the user and potential user of multivariate techniques as well as for postgraduate students coming to the subject for the first time. The emphasis is problem-orientated and stresses geometrical intuition in preference to algebraic manipulation. Mathematical sections, which are not essential for a practical understanding of the techniques, are clearly indicated so that the...

Dallas E. Johnson uses real-life examples to explain the when, why, how, and what of numerous multivariate methods, stressing the importance and practical applications of each method. The result? You come away with a practical understanding of and an ability to do multivariate analysis. By providing annotated SPSS and SAS output along with the computer commands used to produce them, Johnson guides you in relating the results of statistical computing packages to multivariate concepts, and teaches...

This textbook for a second course in basic statistics for undergraduates or first-year graduate students introduces linear regression models and describes other linear models including Poisson regression, logistic regression, proportional hazards regression, and nonparametric regression. Numerous examples drawn from the news and current events with an emphasis on health issues illustrate these concepts. Assuming only a pre-calculus background, the author keeps equations to a minimum and demonstr...

* Includes a new chapter on logistic regression. * Discusses the design and analysis of random trials. * Explores the latest applications of sample size tables. * Contains a new section on binomial distribution.

A Practical Approach to using Multivariate Analyses - The new edition provides advanced undergraduate as well as graduate students with a timely and comprehensive introduction to today's most commonly encountered statistical and multivariate techniques, while assuming only a limited knowledge of higher-level mathematics. KEY TOPIC: Provides hands on guidelines for conducting numerous types of multivariate statistical analyses. Maintains a practical approach, still focusing on the benefits...

Large sets of petroleum fluid data exist for the various reservoir conditions and properties that occur in practice. Petroleum Reservoir Fluid Property Correlations, written by three internationally well-known and respected petroleum engineers, is the result of years of exhaustive research that gathered data sets from databases all over the world. The data were then compared against the results of many published correlations of fluid properties in order to find the ""best in class"" required in...

Starting with the basic linear model where the design and covariance matrices are of full rank, this book demonstrates how the same statistical ideas can be used to explore the more general linear model with rank-deficient design and/or covariance matrices. The unified treatment presented here provides a clearer understanding of the general linear model from a statistical perspective, thus avoiding the complex matrix-algebraic arguments that are often used in the rank-deficient case. Elegant geo...

Provides a systematic account of the subject area, concentrating on the most recent advances in the field. While the focus is on practical considerations, both theoretical and practical issues are explored. Among the advances covered are: regularized discriminant analysis and bootstrap-based assessment of the performance of a sample-based discriminant rule and extensions of discriminant analysis motivated by problems in statistical image analysis. Includes over 1,200 references in the bibliograp...

Multivariate methods are now widely used in the quantitative sciences as well as in statistics because of the ready availability of computer packages for performing the calculations. While access to suitable computer software is essential to using multivariate methods, using the software still requires a working knowledge of these methods and how t

An exploration of regression graphics through computer graphics. Recent developments in computer technology have stimulated new and exciting uses for graphics in statistical analyses. Regression Graphics, one of the first graduate--level textbooks on the subject, demonstrates how statisticians, both theoretical and applied, can use these exciting innovations. After developing a relatively new regression context that requires few scope--limiting conditions, Regression Graphics guides readers thro...