img border="0" src="IMAGES/companionwebsite.jpg" alt="A companion website is available for this text" width="75" height="20" 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 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.

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.

Why is this Book a Useful Supplement for Your Statistics Course? Most core statistics texts cover subjects like analysis of variance and regression, but not in much detail. This book, as part of our Series in Research Methods and Statistics, provides you with the flexibility to cover ANOVA more thoroughly, but without financially overburdening your students.

′This engagingly written and nicely opinionated book is a blend of friendly introduction and concisely applicable detail. No-one can recall every statistical formula, but if they have this book they will know where to look′ - Professor Jon May, University of Plymouth ′This is one of the best books I have come across for teaching introductory statistics. The illustrative examples are engaging and often humorous and the explanations of ′difficult′ concepts are written in a wonderfully clear and intuitive way′ - Nick Allum, University of Essex Selected as an Outstanding Academic Title by Choice Magazine, January 2010 First (and Second) Steps in Statistics, Second Edition provides a clear and concise introduction to the main statistical procedures used in the social and behavioural sciences and is perfect for the statistics student starting their journey. The rationale and procedure for analyzing data are presented through exciting examples with an emphasis on understanding rather than computation. It is ideally suited for introductory courses in statistics given its gentle beginning, yet progressive treatment of topics. In addition to descriptive statistics, graphs, t-tests, oneway ANOVAs, Chi-square, and simple linear regression, this Second Edition now includes some new, more advanced topic areas as well as a host of additional examples to help students confidently progress through their studies and apply the techniques in lab work, reports and research projects. Key features of this new edition: - the reoganization of the first three chapters giving more attention to univariate statistics and providing more examples to work through at this level - more advanced ′second step′ content has been added on factorial ANOVA and multiple regression - the robust methods chapter from the first edition is now spread throughout the book, and is linked with common teaching practices. - many more examples have been added to enhance the book′s practical potential. - a host of exercises as well as further reading sections at the end of every chapter. An accompanying Web page includes information for each chapter using the statistical packages SPSS and R.

"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

Introductory Business Statistics 2e aligns with the topics and objectives of the typical one-semester statistics course for business, economics, and related majors. The text provides detailed and supportive explanations and extensive step-by-step walkthroughs. The author places a significant emphasis on the development and practical application of formulas so that students have a deeper understanding of their interpretation and application of data. Problems and exercises are largely centered on business topics, though other applications are provided in order to increase relevance and showcase the critical role of statistics in a number of fields and real-world contexts. The second edition retains the organization of the original text. Based on extensive feedback from adopters and students, the revision focused on improving currency and relevance, particularly in examples and problems. This is an adaptation of Introductory Business Statistics 2e by OpenStax. You can access the textbook as pdf for free at openstax.org. Minor editorial changes were made to ensure a better ebook reading experience. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution 4.0 International License.

This text presents a comprehensive treatment of basic statistical methods and their applications. It focuses on the analysis of variance and regression, but also addressing basic ideas in experimental design and count data. The book has four connecting themes: similarity of inferential procedures, balanced one-way analysis of variance, comparison of models, and checking assumptions. Most inferential procedures are based on identifying a scalar parameter of interest, estimating that parameter, obtaining the standard error of the estimate, and identifying the appropriate reference distribution. Given these items, the inferential procedures are identical for various parameters. Balanced one-way analysis of variance has a simple, intuitive interpretation in terms of comparing the sample variance of the group means with the mean of the sample variance for each group. All balanced analysis of variance problems are considered in terms of computing sample variances for various group means. Comparing different models provides a structure for examining both balanced and unbalanced analysis of variance problems and regression problems. Checking assumptions is presented as a crucial part of every statistical analysis. Examples using real data from a wide variety of fields are used to motivate theory. Christensen consistently examines residual plots and presents alternative analyses using different transformation and case deletions. Detailed examination of interactions, three factor analysis of variance, and a split-plot design with four factors are included. The numerous exercises emphasize analysis of real data. Senior undergraduate and graduate students in statistics and graduate students in other disciplines using analysis of variance, design of experiments, or regression analysis will find this book useful.

Interaction Effects in Multiple Regression has provided students and researchers with a readable and practical introduction to conducting analyses of interaction effects in the context of multiple regression. The new addition will expand the coverage on the analysis of three way interactions in multiple regression analysis.

A practical `cut to the chase′ handbook that quickly explains the when, where, and how of statistical data analysis as it is used for real-world decision-making in a wide variety of disciplines. In this one-stop reference, the authors provide succinct guidelines for performing an analysis, avoiding pitfalls, interpreting results and reporting outcomes.