Understanding Statistical Analysis

Understanding Statistical Analysis and Modeling is intended for graduate or advanced undergraduate students in the social, behavioral, or managerial sciences who may need to conduct some form of stati

The material in this user-friendly book is presented as simply as possible to ensure that readers will gain a solid understanding of statistical procedures and analysis. The goal of this book is to de

This text provides students with a solid foundation for understanding data analysis and specific statistical techniques. Focusing on the most current and frequently used statistical methods in today's

The book provides complete coverage of the classical methods of statistical analysis. It is designed to give students an understanding of the purpose of statistical analyses, to allow the student to d

This textbook systematically presents fundamental methods of statistical analysis, from probability and statistical distributions, through basic concepts of statistical inference, to a collection of m

The Process of Research and Statistical Analysis in Psychology presents integrated coverage of psychological research methods and statistical analysis to illustrate how these two crucial processes wor

A guide to the issues relevant to the design, analysis, and interpretation of toxicity studies that examine chemicals for use in the environment Statistical Analysis of Ecotoxicity Studies offer

As a part of statistical modeling, regression analysis is the process and technique of understanding the relationship between dependent and independent variables. It is used in forecasting and predict

This edited book focuses on the latest developments in classification, statistical learning, data analysis and related areas of data science, including statistical analysis of large datasets, big data

Statistical Analysis with R For Dummies fills a huge gap in the world of statistical analysis, as well as in the R community. Most guides to R, whether in books or online, focus on R functions and pro

The description for this book, Elementary Statistical Analysis, will be forthcoming.

Introduction to Statistical Analysis of Laboratory Data presents a detailed discussion of important statistical concepts and methods of data presentation and analysis Provides detailed discussions on

Ott and Longnecker's AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, Seventh Edition, provides a broad overview of statistical methods for advanced undergraduate and graduate students from a

"Statistical Applications for Environmental Analysis and Risk Assessment stresses and explains the importance of a basic knowledge of statistics and statistical analysis in the environmental sciences.

Statistical Models and Methods for Reliability and Survival Analysis is a compilation of articles by statistical theory experts that explore developments in survival analysis, statistical goodness of

Statistical Analysis of Management Data provides a comprehensive approach to multivariate statistical analyses that are important for researchers in all fields of management, including finance, produc

The papers in this book cover issues related to the development of novel statistical models for the analysis of data. They offer solutions for relevant problems in statistical data analysis and contai

Six Sigma statistical methodology using Minitab"Problem Solving and Data Analysis using Minitab "presents example-based learning to aid readers in understanding how to use MINITAB 16 for statistical a

Statistical Analysis: Microsoft Excel 2010 “Excel has become the standard platform for quantitative analysis. Carlberg has become a world-class guide for Excel users wanting to do quantitative ana

This book presents models and statistical methods for the analysis of recurrent event data. The authors provide broad, detailed coverage of the major approaches to analysis, while emphasizing the mode

Research Design and Statistical Analysis provides comprehensive coverage of the design principles and statistical concepts necessary to make sense of real data. The book’s goal is to provide a

This book provides an essential understanding of statistical concepts necessary for the analysis of genomic and proteomic data using computational techniques. The author presents both basic and advanc

Statistical power analysis has revolutionized the ways in which we conduct and evaluate research. Similar developments in the statistical analysis of incomplete (missing) data are gaining more widesp

Statistical power analysis has revolutionized the ways in which we conduct and evaluate research. Similar developments in the statistical analysis of incomplete (missing) data are gaining more widesp

This practical guide to survival data and its analysis for readers with a minimal background in statistics shows why the analytic methods work and how to effectively analyze and interpret epidemiologic and medical survival data with the help of modern computer systems. The introduction presents a review of a variety of statistical methods that are not only key elements of survival analysis but are also central to statistical analysis in general. Techniques such as statistical tests, transformations, confidence intervals, and analytic modeling are presented in the context of survival data but are, in fact, statistical tools that apply to understanding the analysis of many kinds of data. Similarly, discussions of such statistical concepts as bias, confounding, independence, and interaction are presented in the context of survival analysis and also are basic components of a broad range of applications. These topics make up essentially a 'second-year', one-semester biostatistics course in

This practical guide to survival data and its analysis for readers with a minimal background in statistics shows why the analytic methods work and how to effectively analyze and interpret epidemiologic and medical survival data with the help of modern computer systems. The introduction presents a review of a variety of statistical methods that are not only key elements of survival analysis but are also central to statistical analysis in general. Techniques such as statistical tests, transformations, confidence intervals, and analytic modeling are presented in the context of survival data but are, in fact, statistical tools that apply to understanding the analysis of many kinds of data. Similarly, discussions of such statistical concepts as bias, confounding, independence, and interaction are presented in the context of survival analysis and also are basic components of a broad range of applications. These topics make up essentially a 'second-year', one-semester biostatistics course in

The statistical analysis of cost-effectiveness data is becoming increasingly important within health and medical research. Statistical Analysis of Cost-Effectiveness Data provides a pra

Statistical Analysis in the Social Sciences imbeds basic descriptive and inferential statistical concepts within a unified framework: the linear regression model. The regression model is used not only

These essays critically reassess the widely accepted view that statistical methods of analysis can, and do, yield causal understanding of social phenomena. They emphasize the historical, philosophical

Statistical Factor Analysis and Related Methods Theory and Applications In bridging the gap between the mathematical and statistical theory of factor analysis, this new work represents the first unifi

A comprehensive introduction to the most commonly used statistical methods relevant in atmospheric, oceanic and climate sciences. Each method is described step-by-step using plain language, and illustrated with concrete examples, with relevant statistical and scientific concepts explained as needed. Particular attention is paid to nuances and pitfalls, with sufficient detail to enable the reader to write relevant code. Topics covered include hypothesis testing, time series analysis, linear regression, data assimilation, extreme value analysis, Principal Component Analysis, Canonical Correlation Analysis, Predictable Component Analysis, and Covariance Discriminant Analysis. The specific statistical challenges that arise in climate applications are also discussed, including model selection problems associated with Canonical Correlation Analysis, Predictable Component Analysis, and Covariance Discriminant Analysis. Requiring no previous background in statistics, this is a highly accessibl

The risks posed by climate change and its effect on climate extremes are an increasingly pressing societal problem. This book provides an accessible overview of the statistical analysis methods which can be used to investigate climate extremes and analyse potential risk. The statistical analysis methods are illustrated with case studies on extremes in the three major climate variables: temperature, precipitation, and wind speed. The book also provides datasets and access to appropriate analysis software, allowing the reader to replicate the case study calculations. Providing the necessary tools to analyse climate risk, this book is invaluable for students and researchers working in the climate sciences, as well as risk analysts interested in climate extremes.

Analysis in Nutrition Research: Principles of Statistical Methodology and Interpretation of the Results describes, in a comprehensive manner, the methodologies of quantitative analysis of data origina

Probabilistic Foundations of Statistical Network Analysis presents a fresh and insightful perspective on the fundamental tenets and major challenges of modern network analysis. Its lucid exposition pr

Probabilistic Foundations of Statistical Network Analysis presents a fresh and insightful perspective on the fundamental tenets and major challenges of modern network analysis. Its lucid exposition pr

The aim of this textbook (previously titled SAS for Data Analytics) is to teach the use of SAS for statistical analysis of data for advanced undergraduate and graduate students in statistics, data sci

An Introduction to Statistical Analyses in Research: With Applications in the Biological and Life Sciences focuses on developing a conceptual foundation of knowledge in statistical analysis while prov

Presents an introduction to the application of statistical analysis. This title covers a range of topics, including uncertainty analysis, graphical and numerical description of datasets, probability,

Conrad Carlberg's Statistical Analysis with R and Microsoft® Excel is the first complete guide to performing modern statistical analyses with Excel, R, or both. Drawing on his immense experience helpi