The complexity, diversity, and random nature of transportation problems necessitates a broad analytical toolbox. Describing tools commonly used in the field, Statistical and Econometric Methods for Transportation Data Analysis, Second Edition provides an understanding of a broad range of analytical tools required to solve transportation problems. It includes a wide breadth of examples and case studies covering applications in various aspects of transportation planning, engineering, safety, and economics. After a solid refresher on statistical fundamentals, the book focuses on continuous dependent variable models and count and discrete dependent variable models. Along with an entirely new section on other statistical methods, this edition offers a wealth of new material. New to the Second Edition A subsection on Tobit and censored regressions An explicit treatment of frequency domain time series analysis, including Fourier and wavelets analysis methods New chapter that presents logistic regression commonly used to model binary outcomes New chapter on ordered probability models New chapters on random-parameter models and Bayesian statistical modeling New examples and data sets Each chapter clearly presents fundamental concepts and principles and includes numerous references for those seeking additional technical details and applications. To reinforce a practical understanding of the modeling techniques, the data sets used in the text are offered on the book’s CRC Press web page. PowerPoint and Word presentations for each chapter are also available for download.
Describing tools commonly used in the field, this textbook provides an understanding of a broad range of analytical tools required to solve transportation problems.
Gibbons, J., Gastwirth, J. (1970). Properties of the percentile modified rank tests. Annals of the Institute of Statistical Mathematics, Suppl. 6, 95–114. Gilbert, C. (1992). A duration model of automobile ownership.
Since the first edition of this book appeared, computers have come to the aid of modern experimenters and data analysts, bringing with them data analysis techniques that were once beyond the calculational reach of even professional ...
3.5.1 pearson's Sample correlation coefficient Let (x1, y1), (x2, y2 ), variables X and ..., Y, where (xn x –, y and n ) denote y – are the a sample corresponding of measurements sample means. on the For example, let X represent spot ...
This book aims at supporting stakeholders to design spatial surveys for agricultural data and/or to analyse the geographically collected data.
This book is an undergraduate text that introduces students to commonly-used statistical methods in economics.
Mastering this material enables readers to: Formulate research hypotheses Identify appropriate statistical and econometric models Avoid common pitfalls and misapplications of statistical methods Interpret model results correctly Ideal as ...
The book shows students how mathematical statistics concepts form the basis of econometric formulations. It also helps them think about statistics as more than a toolbox of techniques.
Spatial Analysis Using Big Data: Methods and Urban Applications helps readers understand the most powerful, state-of-the-art spatial econometric methods, focusing particularly on urban research problems.
The link between social deprivation and the high collision rate of child pedestrians from lower socioeconomic group families can be explained in terms of increased exposure to hazardous environments (Christie 1995b).