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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"--

"This book surveys what executives who make decisions based on forecasts and professionals responsible for forecasts should know about forecasting. It discusses how individuals and firms should think about forecasting and guidelines for good practices. It introduces readers to the subject of time series, presents basic and advanced forecasting models, from exponential smoothing across ARIMA to modern Machine Learning methods, and examines human judgment's role in interpreting numbers and identifying forecasting errors and how it should be integrated into organizations. This is a great book to start learning about forecasting if you are new to the area or have some preliminary exposure to forecasting. Whether you are a practitioner, either in a role managing a forecasting team or at operationally involved in demand planning, a software designer, a student or an academic teaching business analytics, operational research, or operations management courses, the book can inspire you to rethink demand forecasting. No prior knowledge of higher mathematics, statistics, operations research, or forecasting is assumed in this book. It is designed to serve as a first introduction to the non-expert who needs to be familiar with the broad outlines of forecasting without specializing in it. This may include a manager overseeing a forecasting group, or a student enrolled in an MBA program, an executive education course, or programs not specialising in analytics. Worked examples accompany the key formulae to show how they can be implemented"--

1. Introduction. 2. Theoretical underpinnings of regularization methods. 3. Regularization methods for linear models. 4. Regularization methods for generalized linear models. 5. Regularization methods for multivariate linear models. 6. Regularization methods for cluster analysis and principal components analysis. 7. Regularization methods for latent variable models. 8. Regularization methods for multilevel models. 9. Advanced topics in feature selection.

This fifth edition has been expanded and thoroughly updated to reflect recent advances in the field. The emphasis continues to be on exploratory data analysis rather than statistical theory. The coverage offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression. Methods of regression analysis are clearly demonstrated, and examples containing the types of irregularities commonly encountered in the real world are provided. Each example isolates one or two techniques and features detailed discussions of the techniques themselves, the required assumptions, and the evaluated success of each technique.