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Author: Jeff E. Biddle
Publisher: Cambridge University Press
ISBN: 1108492266
Category : Business & Economics
Languages : en
Pages : 349
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Book Description
Recounts the history of a widely used statistical technique in economics, offering insight into how innovative research tools gain acceptance.
Author: Jeff E. Biddle
Publisher: Cambridge University Press
ISBN: 1108492266
Category : Business & Economics
Languages : en
Pages : 349
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Book Description
Recounts the history of a widely used statistical technique in economics, offering insight into how innovative research tools gain acceptance.
Author: Alan Miller
Publisher: CRC Press
ISBN: 1420035932
Category : Mathematics
Languages : en
Pages : 258
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Book Description
Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition promises to continue that tradition. The author ha
Author: Brian L. Weiss, M.D.
Publisher: Hay House, Inc
ISBN: 1401961614
Category : Body, Mind & Spirit
Languages : en
Pages : 97
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Book Description
The benefits of regression therapy extend far beyond the clearing of symptoms. Often, the result is healing at all levels—physical, emotional, and spiritual. Mirrors of Time, by Brian Weiss, M.D., allows you to take regression therapy to the next level. Now you can go back through time by recalling past events that may have led to difficulties in the present. Through the process of remembering, symptoms diminish, and a strong sense of relaxation and well-being often emerges. Even past-life memories can be elicited by these exercises, and regular practice will enhance your physical and emotional health and open up spiritual vistas that can bring new meaning to your life. An audio download is included that goes beyond meditation and visualization exercises—it contains the actual regression techniques Dr. Weiss uses with his patients. By reading Mirrors of Time and practicing the exercises on the accompanying audio, you’ll find that you’ll be filled with more peace, joy, and love—and virtually all aspects of your everyday life will benefit!
Author: Harvey Motulsky
Publisher: Oxford University Press
ISBN: 9780198038344
Category : Mathematics
Languages : en
Pages : 352
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Book Description
Most biologists use nonlinear regression more than any other statistical technique, but there are very few places to learn about curve-fitting. This book, by the author of the very successful Intuitive Biostatistics, addresses this relatively focused need of an extraordinarily broad range of scientists.
Author: Bruce Bernard
Publisher: Phaidon Press
ISBN: 9780714838489
Category : Photography
Languages : en
Pages : 1120
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Book Description
Collects nearly one thousand photographs to present a comprehensive visual document of the twentieth century
Author: Alvin C. Rencher
Publisher: John Wiley & Sons
ISBN: 0470192607
Category : Mathematics
Languages : en
Pages : 690
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Book Description
The essential introduction to the theory and application of linear models—now in a valuable new edition Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed. Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. This modern Second Edition features: New chapters on Bayesian linear models as well as random and mixed linear models Expanded discussion of two-way models with empty cells Additional sections on the geometry of least squares Updated coverage of simultaneous inference The book is complemented with easy-to-read proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS® code for all numerical examples. Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance.
Author: Frost
Publisher: Statistics by Jim Publishing
ISBN:
Category : Computers
Languages : en
Pages : 0
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Book Description
BONUS! Hardcover edition contains a 42-page bonus chapter! Other Multivariate Methods Learn regression analysis at a deeper level with guidance written in everyday language! Intuitively understand regression analysis by focusing on concepts and graphs rather than equations. Learn practical tips for modeling your data and interpreting the results. Feel confident that you're analyzing your data properly and able to trust your results. Know that you can detect and correct problems that arise. Progress from a beginner to a skilled practitioner ready for real-world applications! After an overview of how regression works and why to use it, the book covers a range of topics, including specifying and assessing models, practical applications, types of effects, statistical significance, predictions, and an array of problem-solving techniques. Contains practical and analytical guidance. Select the correct type of regression analysis. Specify the best model and assess how well it fits the data. Interpret the results. Understand main effects, interaction effects, and modeling curvature. Use polynomials, data transformations, and weighted least squares. Generate predictions and evaluate their precision. Check the assumptions and resolve issues. Identify and manage unusual observations. Examples of many regression models and scenarios. Access free downloadable datasets so you can work the examples yourself.
Author: Timothy Z. Keith
Publisher: Routledge
ISBN: 1351667939
Category : Education
Languages : en
Pages : 655
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Book Description
Companion Website materials: https://tzkeith.com/ 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. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. This book: • Covers both MR and SEM, while explaining their relevance to one another • Includes path analysis, confirmatory factor analysis, and latent growth modeling • Makes extensive use of real-world research examples in the chapters and in the end-of-chapter exercises • Extensive use of figures and tables providing examples and illustrating key concepts and techniques New to this edition: • New chapter on mediation, moderation, and common cause • New chapter on the analysis of interactions with latent variables and multilevel SEM • Expanded coverage of advanced SEM techniques in chapters 18 through 22 • International case studies and examples • Updated instructor and student online resources
Author: Amy Allen
Publisher: Columbia University Press
ISBN: 0231540639
Category : Philosophy
Languages : en
Pages : 305
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Book Description
While post- and decolonial theorists have thoroughly debunked the idea of historical progress as a Eurocentric, imperialist, and neocolonialist fallacy, many of the most prominent contemporary thinkers associated with the Frankfurt School—Jürgen Habermas, Axel Honneth, and Rainer Forst—have defended ideas of progress, development, and modernity and have even made such ideas central to their normative claims. Can the Frankfurt School's goal of radical social change survive this critique? And what would a decolonized critical theory look like? Amy Allen fractures critical theory from within by dispensing with its progressive reading of history while retaining its notion of progress as a political imperative, so eloquently defended by Adorno. Critical theory, according to Allen, is the best resource we have for achieving emancipatory social goals. In reimagining a decolonized critical theory after the end of progress, she rescues it from oblivion and gives it a future.
Author: Luca Massaron
Publisher: Packt Publishing Ltd
ISBN: 1783980745
Category : Computers
Languages : en
Pages : 312
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Book Description
Learn the art of regression analysis with Python About This Book Become competent at implementing regression analysis in Python Solve some of the complex data science problems related to predicting outcomes Get to grips with various types of regression for effective data analysis Who This Book Is For The book targets Python developers, with a basic understanding of data science, statistics, and math, who want to learn how to do regression analysis on a dataset. It is beneficial if you have some knowledge of statistics and data science. What You Will Learn Format a dataset for regression and evaluate its performance Apply multiple linear regression to real-world problems Learn to classify training points Create an observation matrix, using different techniques of data analysis and cleaning Apply several techniques to decrease (and eventually fix) any overfitting problem Learn to scale linear models to a big dataset and deal with incremental data In Detail Regression is the process of learning relationships between inputs and continuous outputs from example data, which enables predictions for novel inputs. There are many kinds of regression algorithms, and the aim of this book is to explain which is the right one to use for each set of problems and how to prepare real-world data for it. With this book you will learn to define a simple regression problem and evaluate its performance. The book will help you understand how to properly parse a dataset, clean it, and create an output matrix optimally built for regression. You will begin with a simple regression algorithm to solve some data science problems and then progress to more complex algorithms. The book will enable you to use regression models to predict outcomes and take critical business decisions. Through the book, you will gain knowledge to use Python for building fast better linear models and to apply the results in Python or in any computer language you prefer. Style and approach This is a practical tutorial-based book. You will be given an example problem and then supplied with the relevant code and how to walk through it. The details are provided in a step by step manner, followed by a thorough explanation of the math underlying the solution. This approach will help you leverage your own data using the same techniques.
