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Applied Statistics for Software Managers
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Applied Statistics for Software Managers (Paperback)

£43.99
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Synopsis

Statistical techniques offer immense value to managers and developers who want to maximize quality and efficiency throughout the entire software lifecycle. Finally, there's a guide to using statistical techniques to solve specific software productivity and maintenance problems. Using actual software product data, one of the field's leading experts leads you through every step of the statistical analysis, helping you avoid pitfalls and extract all the value your data has to offer. Katrina Maxwell begins by outlining an intelligent methodology and an exclusive set of "recipes" for analyzing software project data, showing how to answer crucial questions without "getting lost in the data." Starting with actual software project data, organized in a database, Maxwell walks through the entire analysis process, explaining essential techniques such as correlation, regression, and analysis and variance. Along the way, Maxwell presents four real-world case studies focused on the key tasks software project managers face: improving productivity, optimizing time to market, building software development cost models, and identifying software maintenance cost drivers. For all managers, developers, and researchers who want to apply statistical methods to improving the efficiency and quality of their software projects.

Computing & ITComputer programming / software developmentSoftware EngineeringScience & MathematicsMathematicsProbability & statistics Publisher: Pearson Education (US) Publication Date: 14/06/2002 ISBN-13: 9780130417893  Details: Type: Paperback Format: Books
Availability: To Order. Estimated despatch in 3-5 weeks.  

KATRINA D. MAXWELL, Ph.D., a leading international expert in the area of software development productivity, is currently a Partner at Datamax in Fontainebleau, France, specializing in software metrics research, data analysis, and productivity benchmarking. Dr. Maxwell has taught courses in business math and statistics at several academic institutions. She has published research in IEEE Transactions on Software Engineering, IEEE Software, and Management Science.

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