A non-calculus based introduction for students studying statistics, business, engineering, health sciences, social sciences, and education. It presents a thorough coverage of statistical techniques and includes numerous examples largely drawn from actual research studies. Little mathematical background is required and explanations of important concepts are based on providing intuition using illustrative figures and numerical examples. The first part shows how statistical methods are used in diverse fields in answering important questions, while part two covers descriptive statistics and considers the organisation and summarisation of data. Parts three to five cover probability, statistical inference, and more advanced statistical techniques.
This companion to The New Statistical Analysis of Data by Anderson and Finn provides a hands-on guide to data analysis using SPSS. Included with this guide are instructions for obtaining the data sets to be analysed via the World Wide Web.
An Intermediate Course with Examples in S-Plus, R, and SAS Richard M. Heiberger, Burt Holland. (Steel and Torrie, 1960) Steel, R. G. D. and ... (Westfall and Rom, 1990) Westfall, P. H. and Rom, D. (1990). Bootstrap stepdown testing with ...
First half of book presents fundamental mathematical definitions, concepts, and facts while remaining half deals with statistics primarily as an interpretive tool.
Fisher strongly disagreed with the Neyman–Pearson approach, and both that approach and Fisher's own ideas were extensively criticized. Now, approaching a century later, the criticism continues, but various mixtures of the two approaches ...
This book provides an up-to-date treatment of the foundations common to the statistical analysis of network data across the disciplines.
This is the first introductory statistics text to use an estimation approach from the start to help readers understand effect sizes, confidence intervals (CIs), and meta-analysis (‘the new statistics’).
The book includes more than 200 exercises with fully worked solutions. Some familiarity with basic statistical concepts, such as linear regression, is assumed. No previous programming experience is needed.
This book is a guide to the practical application of statistics to data analysis in the physical sciences.
This new edition by two acknowledged experts on the subject offers an up-to-date account of practical methodology for handling missing data problems.
The central package is igraph, which provides extensive capabilities for studying network graphs in R. This text builds on Eric D. Kolaczyk’s book Statistical Analysis of Network Data (Springer, 2009).