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Project Management in Practice, 4th Edition focuses on the technical aspects of project management that are directly related to practice.
Montgomery, Runger, and Hubele provide modern coverage of engineering statistics, focusing on how statistical tools are integrated into the engineering problem-solving process. All major aspects of engineering statistics are covered, including descriptive statistics, probability and probability distributions, statistical test and confidence intervals for one and two samples, building regression models, designing and analyzing engineering experiments, and statistical process control. Developed with sponsorship from the National Science Foundation, this revision incorporates many insights from the authors teaching experience along with feedback from numerous adopters of previous editions.
Projects continue to grow larger, increasingly strategic, and more complex, with greater collaboration, instant feedback, specialization, and an ever-expanding list of stakeholders. Now more than ever, effective project management is critical for the success of any deliverable, and the demand for qualified Project Managers has leapt into nearly all sectors. Project Management provides a robust grounding in essentials of the field using a managerial approach to both fundamental concepts and real-world practice. Designed for business students, this text follows the project life cycle from beginning to end to demonstrate what successful project management looks like on the ground. Expert discussion details specific techniques and applications, while guiding students through the diverse skill set required to select, initiate, execute, and evaluate today's projects. Insightful coverage of change management provides clear guidance on handling the organizational, interpersonal, economic, and technical glitches that can derail any project, while in-depth cases and real-world examples illustrate essential concepts in action.
As the Solutions Manual, this book is meant to accompany the main title, Introduction to Linear Regression Analysis, Fifth Edition. Clearly balancing theory with applications, this book describes both the conventional and less common uses of linear regression in the practical context of today's mathematical and scientific research. Beginning with a general introduction to regression modeling, including typical applications, the book then outlines a host of technical tools that form the linear regression analytical arsenal, including: basic inference procedures and introductory aspects of model adequacy checking; how transformations and weighted least squares can be used to resolve problems of model inadequacy; how to deal with influential observations; and polynomial regression models and their variations. The book also includes material on regression models with autocorrelated errors, bootstrapping regression estimates, classification and regression trees, and regression model validation.