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Auteur : Alex Liu
Catégorie : Ebooks en langues étrangères,Par langue,Ebooks en anglais
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Langue : Français, Anglais

This book is based on a collection of the lecture notes used for author's teaching of regression modeling to Ph.D. candidates in the Marshall School of Business and in the Political Science Department at the University of Southern California, from 2002 to 2004. The book is a friendly and step-bystep guide to anyone who plans to use regression for conducting some kinds of high quality empirical research. In other words, this book is not written for people who just want to think about or to talk about regression modeling. The book is for these students and practitioners who need to use regression to analyze some real data and to produce some insightful research reports. To avoid some of the widely spread confusion in our empirical research field, a RM4EsTM framework has been used to summarize all the main concepts and methods of regression modeling. To state briefly, RM4Es consists of (e1) equations for model representations, (e2) estimation methods for coefficients calculation, (e3) evaluation methods for model assessment, and (e4) explanation for results interpretations. Feedback from the Author's teaching has been continuously confirming that the RM4Es framework is indeed a very effective tool in helping students to gain a clear understanding of regression modeling. Throughout the book, many real research examples have been used to demonstrate all the important regression modeling techniques presented in this book and to keep students updated of the most current applications. Many of these examples are taken from articles published in leading journals such as the American Economic Review and the American Political Science Review. Proper computing is always an important part of any high quality regression-modeling project. For this book, we have selected SPSS and R as our primitive computing tools, because SPSS is easy to use and the powerful R package is FREE. Step by step SPSS or R implementations for the regression techniques presented in this book have been included either in the book content or in the appendix. By using these SPSS or R procedures together with a few datasets included in the book, our readers should be able to test all the important regression techniques and methods to ensure a good mastering of the modern regression modeling methods covered in this book. This book may be used as a textbook for a graduate seminar course or as a training manual for some intensive sessions. Students who completed the course or training should be able to use SPSS or R to analyze their own datasets to produce high quality regression models, as most of my former students did.

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13 Multiple Regression and Model Building / Quantitative ~ 13 Multiple Regression and Model Building. This book focuses on the use of systematic quantitative analysis for purposes of building, refining and testing theoretical propositions in the policy and social sciences. All of the tools discussed so far – including univariate, bi-variate, and simple regression analysis – provide means to .

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5 Applications of Regression Analysis in Business - NewGenApps ~ Data Science understanding is key for predictive analytics. Read More: 8 Ways to Leverage Predictive Analytics for Business. 2. Operation Efficiency: Regression models can also be used to optimize business processes. A factory manager, for example, can create a statistical model to understand the impact of oven temperature on the shelf life of the cookies baked in those ovens. In a call center .

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Regression Analysis and Linear Models: Concepts ~ Regression Analysis and Linear Models: Concepts, Applications, and Implementation (Methodology in the Social Sciences): 9781462521135: Medicine & Health Science Books @ Amazon

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Building Linear Regression Models - Helpful Stats ~ Regression can model the past data, therefore, that same model should be useful to predict the future as well. The ability to use regression to model situations and then predict future outcomes make regression models extremely powerful tools in business. The power of regression models contribute to their massive popularity.

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Regression analysis for the social sciences: Second edition ~ Provides graduate students in the social sciences with the basic skills they need to estimate, interpret, present, and publish basic regression models using contemporary standards.

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Régression avec R / SpringerLink ~ Le niveau mathématique requis rend ce livre accessible aux élèves ingénieurs, aux étudiants de niveau Master et aux chercheurs actifs dans divers domaines des sciences appliquées. Vous pouvez télécharger les exercices corrigés ainsi que les commandes R de l’ouvrage sur ce site (voir rubrique « Informations complémentaires » sur la colonne de droite). Pierre-André Cornillon est .

CHAPTER 1: Basic Concepts of Regression Analysis ~ Linear Regression as a Statistical Model 5. Multiple Linear Regression and Matrix Formulation Introduction I Regression analysis is a statistical technique used to describe relationships among variables. I The simplest case to examine is one in which a variable Y, referred to as the dependent or target variable, may be related to one variable X, called an independent or explanatory variable .

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Regression Analysis - an overview / ScienceDirect Topics ~ Regression analysis is the oldest, and probably, most widely used multivariate technique in the social sciences. Unlike the preceding methods, regression is an example of dependence analysis in which the variables are not treated symmetrically. In regression analysis, the object is to obtain a prediction of one variable, given the values of the others. To accommodate this change of viewpoint .

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How to compare regression models - Duke University ~ When fitting regression models to seasonal time series data and using dummy variables to estimate monthly or quarterly effects, you may have little choice about the number of parameters the model ought to include. You must estimate the seasonal pattern in some fashion, no matter how small the sample, and you should always include the full set, i.e., don’t selectively remove seasonal dummies .