Current Catalog


Book Description

Includes subject section, name section, and 1968-1970, technical reports.




Risk and Decision Analysis in Projects


Book Description

Some of Schuyler's tried-and-true tips include: - The single-point estimate is almost always wrong, so that it is always better to express judgments as ranges. A probability distribution completely expresses someone's judgment about the likelihood of values within the range.- We often need a single-value cost or other assessment, and the expected value (mean) of the distribution is the only unbiased predictor. Expected value is the probability-weighted average, and this statistical idea is the cornerstone of decision analysis.- Some decisions are easy, perhaps aided by quick decision tree calculations on the back of an envelope. Decision dilemmas typically involve risky outcomes, many factors, and the best alternatives having comparable value. We only need analysis sufficient to confidently identify the best alternative. As soon as you know what to do, stop the analysis!- Be alert to ways to beneficially change project risks. We can often eliminate, avoid, transfer, or mitigate threats in some way. Get to know the people who make their living helping managers sidestep risk. They include insurance agents, partners, turnkey contractors, accountants, trainers, and safety personnel.




Decision-making on Mega-projects


Book Description

It will be useful for those experienced and senior professionals who are charged with authorizing and controlling projects. Recommended. P.F. Rad, Choice Building on the seminal work of Bent Flyvbjerg, this book is a collection of expert contributions that will prove essential to anyone wanting to understand why mega-projects go wrong and how they can be made to work better. Professor Sir Peter Hall, University College London, UK This book offers a refreshing and fascinating look at mega-projects from the perspective of public evaluation and planning. With the changing role of the public sector in planning and implementing large-scale projects and a subsequent strong emergence of private public modes of operation, mega-projects have become a problematic phenomenon. This volume is a major source of information and reference. It provides the reader with unique insights and caveats in mega-projects planning. Peter Nijkamp, VU University Amsterdam, The Netherlands This book enlarges the understanding of decision-making on mega-projects and suggest recommendations for a more effective, efficient and democratic approach. Authors from different scientific disciplines address various aspects of the decision-making process, such as management characteristics and cost benefit analysis, planning and innovation and competition and institutions. The subject matter is highly diverse, but certain questions remain at the forefront. For example, how do we deal with protracted preparation processes, how do we tackle risks and uncertainties, and how can we best divide the risks and responsibilities among the private and public players throughout the different phases of the project? Presenting a state-of-the-art overview, based on experiences and visions of authors from Europe and North America, this unique book will be of interest to practitioners of large-scale project management, politicians, public officials and private organisations involved in mega-project decision-making. It will also appeal to researchers, consultants and students dealing with substantial engineering projects, complex systems, project management and transport infrastructure.
















R for Data Science


Book Description

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results




Project News Update


Book Description