Applied Statistics for Software Managers

Applied Statistics for Software Managers

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Description

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.show more

Product details

  • Paperback | 352 pages
  • 175.8 x 233.2 x 23.6mm | 657.72g
  • Pearson Education (US)
  • Prentice Hall
  • Upper Saddle River, United States
  • English
  • 0130417890
  • 9780130417893

About Katrina D. Maxwell

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.show more

Table of contents

Preface. 1. Data Analysis Methodology. Graphs. Tables. Correlation Analysis. Stepwise Regression Analysis. Numerical Variable Checks. Categorical Variable Checks. Testing the Residuals. Detecting Influential Observations.2. Case Study: Software Development Productivity. Creation of New Variables. Data Modifications. Identifying Subsets of Categorical Variables. Model Selection. Graphs. Tables. Correlation Analysis. Stepwise Regression Analysis. Numerical Variable Checks. Categorical Variable Checks. Testing the Residuals. Detecting Influential Observations.3. Case Study: Time to Market. Model Selection. Graphs. Tables. Correlation Analysis. Stepwise Regression Analysis. Numerical Variable Checks. Categorical Variable Checks. Testing the Residuals. Detecting Influential Observations.4. Case Study: Developing a Software Development Cost Model. Choice of Data. Model Selection. Graphs. Tables. Correlation Analysis. Stepwise Regression Analysis. Numerical Variable Checks. Categorical Variable Checks. Testing the Residuals. Detecting Influential Observations. Common Accuracy Statistics. Boxplots of Estimation Error. Wilcoxon Signed-Rank Test. Accuracy Segmentation. The 95% Confidence Interval. Identifying Subsets of Categorical Variables. Model Selection. Building the Multi-Variable Model. Checking the Models. Measuring Estimation Accuracy. Comparison of 1991 and 1993 Models. Management Implications.5. Case Study: Software Maintenance Cost Drivers. It's the Results That Matter. Cost Drivers of Annual Corrective Maintenance (by Katrina D. Maxwell and Pekka Forselius). From Data to Knowledge. Variable and Model Selection. Preliminary Analyses. Building the Multi-Variable Model. Checking the Model. Extracting the Equation. Interpreting the Equation. Accuracy of Model Prediction. The Telon Analysis. Further Analyses. Final Comments.6. What You Need to Know About Statistics. Describing Individual Variables. The Normal Distribution. Overview of Sampling Theory. Other Probability Distributions. Identifying Relationships in the Data. Comparing Two Estimation Models. Final Comments.Appendix A. Raw Software Development Project Data. Appendix B. Validated Software Development Project Data. Appendix C. Validated Software Maintenance Project Data. Index.show more

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