Yu Lei

Gregory J. Privitera

Kristin Lee Sotak

Statistical Analysis "In Focus" supports users of Gregory J. Privitera’s Statistics for the Behavioral Sciences, Third Edition who work with a statistical program other than SPSS or Excel. Three standalone parts, each dedicated to R, SAS, and Stata, serve as step-by-step guides for completing the "In Focus" exercises in Privitera’s core text. A conversational writing style along with "To The Student" introductions allow students to familiarize themselves and become more comfortable with each program prior to making computations. Additionally, General Instruction Guidebook (GIG) sections for R, SAS, and Stata provide standardized how-to instructions for using each program, making the book a valuable reference for students beyond their studies. Bundle and Save Bundle the guide with the core text, Statistics for the Behavioral Sciences, Third Edition, for only $5 more! Order using bundle ISBN 978-1-5443-3027-3.

Alternate Guides for R, SAS, and Stata for Statistics for the Behavioral Sciences

Statistical Analysis "In Focus"

D. Richard Kuhn

Raghu N. Kacker

Combinatorial testing of software analyzes interactions among variables using a very small number of tests. This advanced approach has demonstrated success in providing strong, low-cost testing in real-world situations. Introduction to Combinatorial Testing presents a complete self-contained tutorial on advanced combinatorial testing methods for real-world software. The book introduces key concepts and procedures of combinatorial testing, explains how to use software tools for generating combinatorial tests, and shows how this approach can be integrated with existing practice. Detailed explanations and examples clarify how and why to use various techniques. Sections on cost and practical considerations describe tradeoffs and limitations that may impact resources or funding. While the authors introduce some of the theory and mathematics of combinatorial methods, readers can use the methods without in-depth knowledge of the underlying mathematics. Accessible to undergraduate students and researchers in computer science and engineering, this book illustrates the practical application of combinatorial methods in software testing. Giving pointers to freely available tools and offering resources on a supplementary website, the book encourages readers to apply these methods in their own testing projects.

Introduction to Combinatorial Testing

Maoguo Gong

Qing Cai

Lijia Ma

Shanfeng Wang

This book presents the latest research advances in complex network structure analytics based on computational intelligence (CI) approaches, particularly evolutionary optimization. Most if not all network issues are actually optimization problems, which are mostly NP-hard and challenge conventional optimization techniques. To effectively and efficiently solve these hard optimization problems, CI based network structure analytics offer significant advantages over conventional network analytics techniques. Meanwhile, using CI techniques may facilitate smart decision making by providing multiple options to choose from, while conventional methods can only offer a decision maker a single suggestion. In addition, CI based network structure analytics can greatly facilitate network modeling and analysis. And employing CI techniques to resolve network issues is likely to inspire other fields of study such as recommender systems, system biology, etc., which will in turn expand CI’s scope and applications. As a comprehensive text, the book covers a range of key topics, including network community discovery, evolutionary optimization, network structure balance analytics, network robustness analytics, community-based personalized recommendation, influence maximization, and biological network alignment. Offering a rich blend of theory and practice, the book is suitable for students, researchers and practitioners interested in network analytics and computational intelligence, both as a textbook and as a reference work.

Computational Intelligence for Network Structure Analytics

Yu Lei

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