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"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.

Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- What Are the Aims of the Book? -- What Are the Key Features of the Book? -- The Structure of the Book -- Acknowledgements -- Part I Fundamentals for Modelling Spatial and Spatial-Temporal Data -- 1 Challenges and Opportunities Analysing Spatial and Spatial-Temporal Data -- 1.1 Introduction -- 1.2 Four Main Challenges When Analysing Spatial and Spatial-Temporal Data -- 1.2.1 Dependency -- 1.2.2 Heterogeneity -- 1.2.3 Data Sparsity -- 1.2.4 Uncertainty -- 1.2.4.1 Data Uncertainty