A culmination of the author s many years of consulting and teaching, Design and Analysis of Experiments with SAS provides practical guidance on the computer analysis of experimental data. It connects the objectives of research to the type of experimental design required, describes the actual process of creating the design and collecting the data, shows how to perform the proper analysis of the data, and illustrates the interpretation of results.
Drawing on a variety of application areas, from pharmaceuticals to machinery, the book presents numerous examples...
A culmination of the author s many years of consulting and teaching, Design and Analysis of Experiments with SAS provides practica...
Since the publication of the bestselling, highly recommended first edition, R has considerably expanded both in popularity and in the number of packages available. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition takes advantage of the greater functionality now available in R and substantially revises and adds several topics.
New to the Second Edition
Expanded coverage of binary and binomial responses, including...
Start Analyzing a Wide Range of Problems
Since the publication of the bestselling, highly recommended first edition, R has conside...
Essentials of Probability Theory for Statisticians provides graduate students with a rigorous treatment of probability theory, with an emphasis on results central to theoretical statistics. It presents classical probability theory motivated with illustrative examples in biostatistics, such as outlier tests, monitoring clinical trials, and using adaptive methods to make design changes based on accumulating data. The authors explain different methods of proofs and show how they are useful for establishing classic probability results.
After building a...
Essentials of Probability Theory for Statisticians provides graduate students with a rigorous treatment of probability theory, wit...
Offering 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 experimental design as a whole within the structure provided by general linear models, rather than as a collection of seemingly unrelated solutions to unique problems.
The core material can be found in the first thirteen...
Offering deep insight into the connections between design choice and the resulting statistical analysis, Design of Experiments: An Introduc...
Engineers are expected to design structures and machines that can operate in challenging and volatile environments, while allowing for variation in materials and noise in measurements and signals. Statistics in Engineering, Second Edition: With Examples in MATLAB and R covers the fundamentals of probability and statistics and explains how to use these basic techniques to estimate and model random variation in the context of engineering analysis and design in all types of environments.
The first eight chapters cover probability and probability distributions, graphical displays of data and...
Engineers are expected to design structures and machines that can operate in challenging and volatile environments, while allowing for variation in ma...
