Statistical Distributions, 4th Edition - Softcover

Über die Autorin bzw. den Autor

CATHERINE FORBES, PhD, is Senior Lecturer in the Department of Econometrics and Business Statistics at Monash University (Australia). With experience as a statistical consultant for business, manufacturing industries, and social welfare agencies, her research focuses on the areas of Bayesian econometrics, financial modeling, time series analysis, and model selection.

MERRAN EVANS, PhD, is Professor and Pro Vice-Chancellor (Planning and Quality) at Monash University. Dr. Evans has over thirty years of academic experience in the fields of statistics and econometrics.

NICHOLAS HASTINGS, PhD, is Director and Principal Consultant in physical asset management at Albany Interactive Pty Ltd. Dr. Hastings has published extensively in the areas of engineering management and asset management.

BRIAN PEACOCK, PhD, is Founder of Brian Peacock Ergonomics (BPE) Pte. Ltd., a Singapore-based firm that offers ergonomics and human factors education, training, and consulting services. He has previously held consulting positions at General Motors Company and the National Space Biomedical Research Institute/NASA.

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A new edition of the trusted guide on commonly used statistical distributions

Fully updated to reflect the latest developments on the topic, Statistical Distributions, Fourth Edition continues to serve as an authoritative guide on the application of statistical methods to research across various disciplines. The book provides a concise presentation of popular statistical distributions along with the necessary knowledge for their successful use in data modeling and analysis.

Following a basic introduction, forty popular distributions are outlined in individual chapters that are complete with related facts and formulas. Reflecting the latest changes and trends in statistical distribution theory, the Fourth Edition features:

• A new chapter on queuing formulas that discusses standard formulas that often arise from simple queuing systems

• Methods for extending independent modeling schemes to the dependent case, covering techniques for generating complex distributions from simple distributions

• New coverage of conditional probability, including conditional expectations and joint and marginal distributions

• Commonly used tables associated with the normal (Gaussian), student-t, F and chi-square distributions

• Additional reviewing methods for the estimation of unknown parameters, such as the method of percentiles, the method of moments, maximum likelihood inference, and Bayesian inference

Statistical Distributions, Fourth Edition is an excellent supplement for upper-undergraduate and graduate level courses on the topic. It is also a valuable reference for researchers and practitioners in the fields of engineering, economics, operations research, and the social sciences who conduct statistical analyses.