Analysis Of Variance Via Confidence Intervals

Analysis of variance (ANOVA) constitutes the main set of statistical methods used by students and researchers to analyse data from experiments. This expertly written textbook adopts a pioneering approach to ANOVA with an emphasis on confidence intervals rather than tests of significance. Key features of the book include: · Extensive coverage · Strong emphasis upon practical examples · Web-based links to sample questions and answers Student-focused throughout, it offers a comprehensive introduction to ANOVA using confidence intervals. The chapters have been organized to fit onto a typical lecture programme and is well-structured and practical, invaluable for undergraduates and postgraduate students taking courses in quantitative methods across the social sciences.

Analysis of variance - ANOVA - constitutes the main set of statistical methods through which undergraduate and postgraduate students carry out multivariate analysis. This textbook adopts an innovative approach to ANOVA, placing emphasis on confidence intervals rather than tests of significance.

Summarizes information scattered in the technical literature on a subject too new to be included in most textbooks, but which is of interest to statisticians, and those who use statistics in science and education, at an advanced undergraduate or higher level. Overviews recent research on constructin

This book presents guidelines for the development and evaluation of statistical software designed to ensure minimum acceptable statistical functionality as well as ease of interpretation and use. It consists of the proceedings of a forum that focused on three qualities of statistical software: richnessâ€"the availability of layers of output sophistication, guidanceâ€"how the package helps a user do an analysis and do it well, and exactnessâ€"determining if the output is "correct" and when and how to warn of potential problems.

This book provides a protocol for conducting gauge repeatability and reproducibility (R&R) experiments. Such an experiment is required whenever a new test system is developed to monitor a manufacturing process. The protocol presented here is used to determine if the testing system is capable of monitoring the manufacturing process with the desired level of accuracy and precision. This protocol is not currently available in other books or technical reports. In addition to providing a protocol for testing a measurement system, the book presents an up-to-date summary of methods used to construct confidence intervals in normal-based random and mixed analysis of variance (ANOVA) models. Thus, this comprehensive book will be useful to scientists in all fields of application who wish to construct interval estimates for ANOVA model parameters. It includes approaches that can be applied to any ANOVA model.

"Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com

This highly popular introduction to confidence intervals has been thoroughly updated and expanded. It includes methods for using confidence intervals, with illustrative worked examples and extensive guidelines and checklists to help the novice.