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Intro -- Title Page -- Copyright -- Table of Contents -- Preface -- Organisation of the book -- Additional material available online -- Suggested routes through the book -- Acknowledgements -- Chapter 1: Introduction -- 1.1 What is an index number? -- 1.2 Example - the Consumer Prices Index -- 1.3 Example - FTSE 100 -- 1.4 Example - Multidimensional Poverty Index -- 1.5 Example - Gender Inequality Index -- 1.6 Representing the world with index numbers -- 1.7 Chapter summary -- References -- Chapter 2: Index numbers and change -- 2.1 Calculating an index series from a data series -- 2.2 Calculating percentage change -- 2.3 Comparing data series with index numbers -- 2.4 Converting from an index series to a data series -- 2.5 Chapter summary -- Exercise A -- Chapter 3: Measuring inflation -- 3.1 What is inflation? -- 3.2 What are inflation measures used for and why are they important? -- 3.3 Chapter summary -- References -- Exercise B -- Chapter 4: Introducing price and quantity -- 4.1 Measuring price change -- 4.2 Simple, un-weighted indices for price change -- 4.3 Price, quantity and value -- 4.4 Example - Retail Sales Index -- 4.5 Chapter summary -- Exercise C -- Chapter 5: Laspeyres and Paasche indices -- 5.1 The Laspeyres price index -- 5.2 The Paasche price index -- 5.3 Laspeyres and Paasche quantity indices -- 5.4 Laspeyres and Paasche: mind your Ps and Qs -- 5.5 Laspeyres, Paasche and the Index Number Problem -- 5.6 Laspeyres or Paasche? -- 5.7 A more practical alternative to a Laspeyres price index? -- 5.8 Chapter summary -- References -- Exercise D -- Chapter 6: Domains and aggregation -- 6.1 Defining domains -- 6.2 Indices for domains -- 6.3 Aggregating domains -- 6.4 More complex aggregation structures -- 6.5 A note on aggregation structures in practice -- 6.6 Non-consistency in aggregation -- 6.7 Chapter summary -- Exercise E.

"Incorporating global research from the field, this book summarizes the current best advice and points out recommended testing and monitoring methods for business surveys. Organized into two sections on Designing and Conducting, it introduces questions that address important conceptual distinctions and covers topics like systematic errors, focus groups, primary and mixed-mode data collection issues, contact strategies, web survey, development and testing methods, data collection instruments, conduct, procedures, administration, and more. It is an ideal book for researchers and data collection methodologists, as well as students"--

Front Matter -- Structural Equation Modeling -- Structural Equation Modeling Software -- Steps in Structural Equation Modeling -- Advanced Topics: Principles and Applications -- References -- Index -- Other titles from iSTE in Mathematics and Statistics

"More than 300 exercises at the end of each chapter provide the opportunity for readers to apply new concepts and test their knowledge. Answers for selected exercises (at the rear of the book) offer additional insights to help readers consolidate their understanding"--

Thinking statistically about crime -- Homicide -- Police statistics -- National crime victimization survey -- Sampling principles and the ncvs -- NCVS measurement and missing data -- Judging the quality of a statistic -- Sexual assault -- Fraud and identity theft -- Big data and crime statistics -- Crime statistics, 1915 and beyond.

The R Companion to Elementary Applied Statistics includes traditional applications covered in elementary statistics courses as well as some additional methods that address questions that might arise during or after the application of commonly used methods. Beginning with basic tasks and computations with R, readers are then guided through ways to bring data into R, manipulate the data as needed, perform common statistical computations and elementary exploratory data analysis tasks, prepare customized graphics, and take advantage of R for a wide range of methods that find use in many elementary applications of statistics. Features: Requires no familiarity with R or programming to begin using this book. Can be used as a resource for a project-based elementary applied statistics course, or for researchers and professionals who wish to delve more deeply into R. Contains an extensive array of examples that illustrate ideas on various ways to use pre-packaged routines, as well as on developing individualized code. Presents quite a few methods that may be considered non-traditional, or advanced. Includes accompanying carefully documented script files that contain code for all examples presented, and more. R is a powerful and free product that is gaining popularity across the scientific community in both the professional and academic arenas. Statistical methods discussed in this book are used to introduce the fundamentals of using R functions and provide ideas for developing further skills in writing R code. These ideas are illustrated through an extensive collection of examples. About the Author: Christopher Hay-Jahans received his Doctor of Arts in mathematics from Idaho State University in 1999. After spending three years at University of South Dakota, he moved to Juneau, Alaska, in 2002 where he has taught a wide range of undergraduate courses at University of Alaska Southeast.

Cost-effectiveness analysis is becoming an increasingly important tool for decision making in the health systems. Cost-Effectiveness of Medical Treatments formulates the cost-effectiveness analysis as a statistical decision problem, identifies the sources of uncertainty of the problem, and gives an overview of the frequentist and Bayesian statistical approaches for decision making. Basic notions on decision theory such as space of decisions, space of nature, utility function of a decision and optimal decisions, are explained in detail using easy to read mathematics. Features Focuses on cost-effectiveness analysis as a statistical decision problem and applies the well-established optimal statistical decision methodology. Discusses utility functions for cost-effectiveness analysis. Enlarges the class of models typically used in cost-effectiveness analysis with the incorporation of linear models to account for covariates of the patients. This permits the formulation of the group (or subgroup) theory. Provides Bayesian procedures to account for model uncertainty in variable selection for linear models and in clustering for models for heterogeneous data. Model uncertainty in cost-effectiveness analysis has not been considered in the literature. Illustrates examples with real data. In order to facilitate the practical implementation of real datasets, provides the codes in Mathematica for the proposed methodology. The motivation for the book is to make the achievements in cost-effectiveness analysis accessible to health providers, who need to make optimal decisions, to the practitioners and to the students of health sciences. Elaias Moreno is Professor of Statistics and Operational Research at the University of Granada, Spain, Corresponding Member of the Royal Academy of Sciences of Spain, and elect member of ISI. Francisco Josae Vaazquez-Polo is Professor of Mathematics and Bayesian Methods at the University of Las Palmas de Gran Canaria, and Head of the Department of Quantitative Methods. Miguel aAngel Negrain is Senior Lecturer in the Department of Quantitative Methods at the ULPGC. His main research topics are Bayesian methods applied to Health Economics, economic evaluation and cost-effectiveness analysis, meta-analysis and equity in the provision of healthcare services.

"There are a growing number of researchers and analysts who find the probability-based approaches for assessing risk and uncertainties to be too narrow and limiting. Uncertainty in Risk Assessment provides a broad conceptual framework and describes various alternative approaches of uncertainty representation and characterization therein such as probability-bound analysis, imprecise probability and evidence theory. The authors, whose own research has been at the forefront of developments in the field, include a number of real-life applications which demonstrate the practical use of the various methods in the different realistic circumstances. They provide invaluable practical guidance and clear recommendations on how and when to use the various approaches"--

Like its bestselling predecessor, Multilevel Modeling Using R, Second Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single level and multilevel data. This thoroughly updated revision gives the reader state-of-the-art tools to launch their own investigations in multilevel modeling and gain insight into their research.