General

Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical...

This book is ideal for courses on Probability typically taught in Mathematics and/or Statistics departments but could also be used in Engineering or Data Science departments. This book could also serve as a supplemental or review text for courses on...

Solutions Manual to accompany Statistical Data Analytics: Foundations for Data Mining, Informatics, and Knowledge Discovery A comprehensive introduction to statistical methods for data mining and knowledge discovery. Extensive solutions using actual data...

Provides an easy-to-understand guide to statistical linear models and its uses in data analysis This book defines a broad spectrum of statistical linear models that is useful in the analysis of data. Considerable rewriting was done to make the book more...

In the past, disease pattern mapping depended on census tracts based on political units, such as states and counties. However, with the advent of geographic information systems (GIS), researchers can now achieve a new level of precision and flexibility...

Maintaining the excellent features that made the first edition so popular, this outstandingreference/text presents the only comprehensive treatment of the theory ofpoint processes and statistical inference for point processes-highlighting both...

The book is written for anyone who wants to design experiments, carry them out, and analyze the results. The authors provide a clear-cut, practical approach to designing experiments in any discipline and explain the general principles upon which such...

Considered highly exotic tools as recently as the late 1990s, microarrays are now ubiquitous in biological research. Traditional statistical approaches to design and analysis were not developed to handle the high-dimensional, small sample problems posed...

An understanding and working knowledge of the basic principles of statistics are of central importance in understanding the sport and health sciences. Introduction to Statistics in Human Performance: Using SPSS and R provides students facing statistical...

Focussing on the work of Sir John Kingman, one of the world's leading researchers in probability and mathematical genetics, this book touches on the important areas of these subjects in the last 50 years. Leading authorities give a unique insight into a...

A comprehensive introduction to bootstrap methods in the R programming environment Bootstrap methods provide a powerful approach to statistical data analysis, as they have more general applications than standard parametric methods. An Introduction to...

Presents the basic statistical principles that are necessary to analyze the probabilistic nature of queues Thoroughly revised and expanded to reflect the latest developments in the field, the fourth edition of Fundamentals of Queueing Theory illustrates...

Longitudinal surveys are surveys that involve collecting data from multiple subjects on multiple occasions. They are typically used for collecting data relating to social, economic, educational and health-related issues and they serve as an important...

Analyzing Longitudinal Clinical Trial Data: A Practical Guide provides practical and easy to implement approaches for bringing the latest theory on analysis of longitudinal clinical trial data into routine practice.The book, with its example-oriented...

Analyzing Health Data in R for SAS Users is aimed at helping health data analysts who use SAS accomplish some of the same tasks in R. It is targeted to public health students and professionals who have a background in biostatistics and SAS software, but...

Multiple Imputation in Practice: With Examples Using IVEware provides practical guidance on multiple imputation analysis, from simple to complex problems using real and simulated data sets. Data sets from cross-sectional, retrospective, prospective and...

Data Analytics: A Small Data Approach is suitable for an introductory data analytics course to help students understand some main statistical learning models. It has many small datasets to guide students to work out pencil solutions of the models and...

The book gives an up-to-date account of various approaches availablefor the analysis of infectious disease data. Most of the methods havebeen developed only recently, and for those based on particularlymodern mathematics, details of the computation are...

Throughout the physical and social sciences, researchers face the challenge of fitting statistical distributions to their data. Although the study of statistical modelling has made great strides in recent years, the number and variety of distributions to...

"Offers a comprehensive, unified presentation of statistical designs and methods of analysis for all stages of pharmaceutical development--emphasizing biopharmaceutical applications and demonstrating statistical techniques with real-world examples...

This remarkable text raises the analysis of data in health sciences and policy to new heights of refinement and applicability by introducing cutting-edge meta-analysis strategies while reviewing more commonly used techniques. Each chapter builds on sound...

Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood Approach presents a unified Bayesian treatment of parameter inference and model comparisons that can be used with simple diffuse prior specifications. This...

Methods of Statistical Model Estimation examines the most important and popular methods used to estimate parameters for statistical models and provide informative model summary statistics. Designed for R users, the book is also ideal for anyone wanting...

Examining the strengths and limitations of various standards of accuracy in clinical laboratory analyses, this detailed reference presents an in-depth study of important theoretical and empirical issues concerning the description, collection, and...

The Future of Clinical Research and Health Care: From Empirical to Precision Medicine Clinical and Statistical Considerations in Personalized Medicine explores recent advances related to biomarkers and their translation into clinical development. Leading...

Handbook of Survival Analysis presents modern techniques and research problems in lifetime data analysis. This area of statistics deals with time-to-event data that is complicated by censoring and the dynamic nature of events occurring in time.With...

Statistical methods that are commonly used in the review and approval process of regulatory submissions are usually referred to as statistics in regulatory science or regulatory statistics. In a broader sense, statistics in regulatory science can be...

Starting with the basic linear model where the design and covariance matrices are of full rank, this book demonstrates how the same statistical ideas can be used to explore the more general linear model with rank-deficient design and/or covariance...

'What makes this book unique among books of similar size and scope is that when the author decided to include something in the book, he has treated it in a way similar to the common practice in textbooks, with very detailed and reader-friendly...

This title includes a number of Open Access chapters. Climate change will severely impact the world's food supply unless steps are taken to increase crop resilience. Otherwise, the negative effects on both the yield and the quality of crop plants...

Examines Critical Decisions for Transitioning Lab Science to a Clinical Setting The development of therapeutic pharmaceutical compounds is becoming more expensive, and the success rates for getting such treatments approved for marketing and to the...

Illustrating a simple, novel method for solving an array of statistical problems, Observed Confidence Levels: Theory and Application describes the basic development of observed confidence levels, a methodology that can be applied to a variety of common...

It's been over a decade since the first edition of Measurement Error in Nonlinear Models splashed onto the scene, and research in the field has certainly not cooled in the interim. In fact, quite the opposite has occurred. As a result, Measurement Error...

Written by renowned experts in the field, Sampling Strategies for Natural Resources and the Environment covers the sampling techniques used in ecology, forestry, environmental science, and natural resources. The book presents methods to estimate...

Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling and will promote advances in research and a greater understanding between the mechanistic and the statistical modeling...

Requiring no prior training, Modern Statistics for the Social and Behavioral Sciences provides a two-semester, graduate-level introduction to basic statistical techniques that takes into account recent advances and insights that are typically ignored in...

A graphical model is a statistical model that is represented by a graph. The factorization properties underlying graphical models facilitate tractable computation with multivariate distributions, making the models a valuable tool with a plethora of...

A Training Tool for the Environmental Risk Professional Environmental Risk Analysis: Probability Distribution Calculations defines the role that probability distributions play in risk analysis, and gives direction on how to measure and compare the...

Create Top-Quality Figures Using SAS Producing High-Quality Figures Using SAS/GRAPH (R) and ODS Graphics Procedures gives statisticians and SAS programmers practical guidance on presenting research data in high-quality figures that meet the publication...

Age-Period-Cohort analysis has a wide range of applications, from chronic disease incidence and mortality data in public health and epidemiology, to many social events (birth, death, marriage, etc) in social sciences and demography, and most recently...