Includes bibliographical references (pages 395-412) and index.
CONTENTS NOTE
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Part I. Fundamentals -- 1. Statistical inference I: Descriptive statistics -- 2. Statistical inference II: Interval estimation, hypothesis testing, and population comparisons -- Part II. Continuous dependent variable models -- 3. Linear regression -- 4. Violations of regression assumptions -- 5. Simultaneous equation models -- 6. Panel data analysis -- 7. Time-series analysis -- 8. Latent variable models -- 9. Duration models -- Part III. Count and discrete dependent variable models -- 10. Count data models -- 11. Discrete outcome models -- 12. Discrete/continuous models -- Appendixes: -- A. Statistical fundamentals -- B. Glossary of terms -- C. Statistical tables -- D. Variable transformations.
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SUMMARY OR ABSTRACT
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As the field of transportation moves toward the "total quality management" paradigm, performance-based outcomes and quantitative measures have become increasingly important. Measuring performance in the field depends heavily on modeling trends and data, which in turn requires powerful, and flexible analytical tools. To date transportation professionals have lacked a unified, rigorous guide to modeling the wide range of problems they encounter in the field. This book describes the techniques most useful for modeling the many complex aspects of transportation, such as travel demand, safety, emissions, and the environment. It makes three unique contributions to transportation practice and education. It presents a host of analytical techniques-both common and sophisticated-used to model transportation phenomena; it provides a wealth of examples and case studies, and it specifically targets present and future transportation professionals. It builds the foundation needed not only to apply analytical models but also to understand and interpret results published elsewhere.