Автор: Keith Название: Multiple Regression and Beyond ISBN: 1138061441 ISBN-13(EAN): 9781138061446 Издательство: Taylor&Francis Рейтинг: Цена: 13014.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Multiple Regression and Beyond offers a conceptually oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods.
Описание: This book reviews the three most popular methods (and their extensions) in applied economics and other social sciences: matching, regression discontinuity, and difference in differences. The book introduces the underlying econometric/statistical ideas, shows what is identified and how the identified parameters are estimated, and then illustrates how they are applied with real empirical examples. The book emphasizes how to implement the three methods with data: many data and programs are provided in the online appendix. All readers---theoretical econometricians/statisticians, applied economists/social-scientists and researchers/students---will find something useful in the book from different perspectives.
Описание: With the rise of "big data," there is an increasing demand to learn the skills needed to undertake sound quantitative analysis without requiring students to spend too much time on high-level math and proofs. This book provides an efficient alternative approach, with more time devoted to the practical aspects of regression analysis and how to recognize the most common pitfalls. By doing so, the book will better prepare readers for conducting, interpreting, and assessing regression analyses, while simultaneously making the material simpler and more enjoyable to learn. Logical and practical in approach, Regression Analysis teaches: (1) the tools for conducting regressions; (2) the concepts needed to design optimal regression models (based on avoiding the pitfalls); and (3) the proper interpretations of regressions. Furthermore, this book emphasizes honesty in research, with a prevalent lesson being that statistical significance is not the goal of research. This book is an ideal introduction to regression analysis for anyone learning quantitative methods in the social sciences, business, medicine, and data analytics. It will also appeal to researchers and academics looking to better understand what regressions do, what their limitations are, and what they can tell us. This will be the most engaging book on regression analysis (or Econometrics) you will ever read!
Описание: This book offers a new perspective on argumentative writing in grades 9-12, a set of principled practices, and case studies of excellent teaching to guide educators in using research-based knowledge to foster thoughtful writing and analytic thinking in high school classrooms.
Автор: Miles J & Shevlin M Название: Applying Regression and Correlation ISBN: 0761962301 ISBN-13(EAN): 9780761962304 Издательство: Sage Publications Рейтинг: Цена: 8712.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: By introducing the reader to regression analysis through a simple model-building approach, this book takes a fresh look at applying regression analysis in the behavioural sciences.
Автор: John O`Quigley Название: Proportional Hazards Regression ISBN: 1441920455 ISBN-13(EAN): 9781441920454 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book focuses on the theory and applications of a very broad class of models which underlie modern survival analysis. However, this text differs from most recent works in that it is mostly concerned with methodological issues rather than the analysis itself.
Автор: J.H.F. Schilderinck Название: Regression and factor analysis applied in econometrics ISBN: 1461340535 ISBN-13(EAN): 9781461340539 Издательство: Springer Рейтинг: Цена: 12157.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book deals with the methods and practical uses of regression and factor analysis. Besides the model for the Netherlands published here, the models for Belgium, Italy, West Germany and the United Kingdom are ready for linking and for publishing later on.
Автор: Bernd Fitzenberger; Roger Koenker; Jose A.F. Macha Название: Economic Applications of Quantile Regression ISBN: 3790825026 ISBN-13(EAN): 9783790825022 Издательство: Springer Рейтинг: Цена: 24456.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Complementing classical least squares regression methods which are designed to estimate conditional mean models, quantile regression provides an ensemble of techniques for estimating families of conditional quantile models, thus offering a more complete view of the stochastic relationship among variables.
Автор: Sheather, Simon J. Название: Modern approach to regression with r ISBN: 1441918728 ISBN-13(EAN): 9781441918727 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book focuses on tools and techniques for building valid regression models using real-world data. A key theme throughout the book is that it only makes sense to base inferences or conclusions on valid models.
Автор: Frantisek Stulajter Название: Predictions in Time Series Using Regression Models ISBN: 1441929657 ISBN-13(EAN): 9781441929655 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book will interest and assist people who are dealing with the problems of predictions of time series in higher education and research. It will greatly assist people who apply time series theory to practical problems in their work and also serve as a textbook for postgraduate students in statistics economics and related subjects.
Автор: Yadolah Dodge; Jana Jureckova Название: Adaptive Regression ISBN: 1461264642 ISBN-13(EAN): 9781461264644 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: While there have been a large number of estimation methods proposed and developed for linear regression, none has proved good for all purposes. This text focuses on the construction of an adaptive combination of two estimation methods so as to help users make an objective choice and combine the desirable properties of two estimators.
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