Probability & Statistics

In this second edition of Counterfactuals and Causal Inference, completely revised and expanded, the essential features of the counterfactual approach to observational data analysis are presented with examples from the social, demographic, and health...

Why is it that incredibly unlikely phenomena actually happen quite regularly and why should we, in fact, expect such things to happen? Here, in this highly original book - aimed squarely at anyone with an interest in coincidences, probability or gambling...

Aims to provide in-depth descriptions of the latest developments in multiple comparison methods and selection procedures, while emphasizing biometry. This text is published in honour of the 70th birthday of Charles W. Dunnett - a pioneer in statistical...

Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities...

Statistics for Linguists: An Introduction Using R is the first statistics textbook on linear models for linguistics. The book covers simple uses of linear models through generalized models to more advanced approaches, maintaining its focus on conceptual...

Have you been told you need to do multilevel modeling, but you can't get past the forest of equations? Do you need the techniques explained with words and practical examples so they make sense? Help is here! This book unpacks these statistical...

Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or mean imputation, only work under highly restrictive conditions, which are often not met in practice. Multiple imputation replaces each missing value by...

Built specifically for statistical computing and graphics, the R language, along with its amazing collection of libraries and tools, is one of the most powerful tools you can use to tackle data analysis for business, research, and other data-intensive...

Packed with practical tips and techniques for solving probability problems Increase your chances of acing that probability exam -- or winning at the casino! Whether you're hitting the books for a probability or statistics course or hitting the tables...

Originally published in 1986, this valuable reference provides a detailed treatment of limit theorems and inequalities for empirical processes of real-valued random variables. It also includes applications of the theory to censored data, spacings, rank...

'Lucid, calm, informed, directly helpful in trying to think about where we are now... The literature of the time after begins here' Evening Standard'Taking a breather from bewildering statistics and terrible tales of contagion to read Giordano's book was...

Based on a starter course for beginning graduate students, Core Statistics provides concise coverage of the fundamentals of inference for parametric statistical models, including both theory and practical numerical computation. The book considers both...

Value of Information for Healthcare Decision-Making introduces the concept of Value of Information (VOI) use in health policy decision-making to determine the sensitivity of decisions to assumptions, and to prioritise and design future research. These...

Apply machine learning using the Internet of Things (IoT) in the agriculture, telecom, and energy domains with case studies. This book begins by covering how to set up the software and hardware components including the various sensors to implement the...

Based on a lecture course given at Chalmers University of Technology, this 2002 book is ideal for advanced undergraduate or beginning graduate students. The author first develops the necessary background in probability theory and Markov chains before...

In a new textbook designed for students new to statistics and social data, Stephen Gorard focuses on non-inferential statistics as a basis to ensure students have basic statistical literacy. Understanding why we have to learn statistics and seeing...

This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed...

From crime figures to health scares, election polls to stock market forecasts, numbers make the news all the time. But are they accurate? John Allen Paulos, travels through the pages of an average newspaper, revealing how mathematics is at the heart of...

Statistics 1 was to provide thorough preparation for the revised 2004 specification. Based on the first editions, this series help you to prepare for the new exams.Book InformationISBN 9780435513382Author Roger WilliamsonFormat PaperbackPage Count...

The Fourth Edition has been carefully revised and updated to reflect current data.About the AuthorDavid Freedman received his B.Sc. from McGill and his Ph.D. from Princeton. He has worked as a consultant for the City of San Francisco, the County of Los...

Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion,...

This lively book lays out a methodology of confidence distributions and puts them through their paces. Among other merits, they lead to optimal combinations of confidence from different sources of information, and they can make complex models amenable to...

Surrogates: a graduate textbook, or professional handbook, on topics at the interface between machine learning, spatial statistics, computer simulation, meta-modeling (i.e., emulation), design of experiments, and optimization. Experimentation through...

Every business area relies on an understanding of statistics to succeed. Statistics for Business and Economics by Carlos Cortinhas and Ken Black shows students that the proper application of statistics in the business world goes hand-in-hand with good...

Capture-recapture methods have been used in biology and ecology for more than 100 years. However, it is only recently that these methods have become popular in the social and medical sciences to estimate the size of elusive populations such as illegal...

Written in an accessible and clear manner, Straightforward Statistics with Excel (R) 2e by Chieh-Chen Bowen helps students across the social and behavioral sciences gradually build their skills to develop a better understanding of the world around them...

Markov chains are central to the understanding of random processes. This is not only because they pervade the applications of random processes, but also because one can calculate explicitly many quantities of interest. This textbook, aimed at advanced...

Modelling with the Ito integral or stochastic differential equations has become increasingly important in various applied fields, including physics, biology, chemistry and finance. However, stochastic calculus is based on a deep mathematical theory.This...

Introductory Statistics, Fourth Edition, reviews statistical concepts and techniques in a manner that will teach students not only how and when to utilize the statistical procedures developed, but also how to understand why these procedures should be...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9781476784885Author Dr Hannah FryFormat HardbackPage Count 128Imprint Simon & Schuster/ TedPublisher Simon & Schuster/ TedWeight(grams) 249gDimensions(mm)...

Mathematics is beautiful--and it can be fun and exciting as well as practical. Good Math is your guide to some of the most intriguing topics from two thousand years of mathematics: from Egyptian fractions to Turing machines; from the real meaning of...

Modern statistics is very different from the dry and dusty discipline of the popular imagination. In its place is an exciting subject which uses deep theory and powerful software tools to shed light and enable understanding. And it sheds this light on...

Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and...

Build predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting. In Time Series Forecasting in Python you will learn how to: Recognize a...

There is a new edition of this book available: Even You Can Learn Statistics and Analytics: An Easy to Understand Guide, 4th Edition (ISBN-13: 978-0-13-765476-5). Thought you couldn't learn statistics? You can - and you will! Even You Can Learn...

The Second Edition of Advanced Issues in Partial Least Squares Structural Equation Modeling offers a straightforward and practical guide to PLS-SEM for users ready to go further than the basics of the Primer by the same author team. This brief text...

Praise for the First Edition: "The book makes a valuable contribution by synthesizing current research and identifying areas for future investigation for each aspect of the survey process." -Journal of the American Statistical Association "Overall, the...

This revised textbook motivates and illustrates the techniques of applied probability by applications in electrical engineering and computer science (EECS). The author presents information processing and communication systems that use algorithms based on...

Praise for the Third Edition ". . . an easy-to read introduction to survival analysis which covers the major concepts and techniques of the subject." -Statistics in Medical Research Updated and expanded to reflect the latest developments, Statistical...

Over the past decade or so, we have seen a multitude of improvement programmes and projects to improve the safety of patient care in healthcare. However, the full potential of these efforts and especially those that seek to address an entire system has...