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Cet ouvrage expose de mani re d taill e l une des m thodes statistiques les plus courantes: la r gression. Apr?'s avoir pr sent la r gression lin aire simple et multiple, il s attache expliquer les fondements de la m thode, tant au niveau des choix op r?'s que des hypoth ses et de leur utilit . Ensuite sont d velopp?'s les outils permettant de v rifier les hypoth ses de base mises en uvre par la r gression. Une pr sentation simple des mod les d'analyse de la covariance et de la variance est effectu e. L ouvrage pr sente aussi les choix de mod les et certaines extensions de la r gression: lasso

Regression analysis. --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling

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Mathematical Statistics --- Mathematics --- Physical Sciences & Mathematics --- Regression analysis. --- Robust statistics. --- Statistics, Robust --- Distribution (Probability theory) --- Mathematical statistics --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling

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Das Buch vereinigt die zahlreichen Ansätze zur Erklärung einer Menge von Variablen mittels einer anderen Variablenmenge. Die Ansätze werden in ihren Grundstrukturen dargestellt. Die Beschränkung darauf ist notwendig, gibt es doch zu jeder der vorgestellten Methoden eigene Monographien. Auf diese Spezialliteratur wird für weitergehende Aspekte verwiesen. Die zahlreichen Beispiele werden mit der freien statistischen Programmierumgebung R durchgerechnet; dazu wird der R-Code angegeben. Die überwältigende Anzahl von Funktionen in R erlaubt es, die Vielfalt der hier besprochenen Methoden mit R allein und ohne Programmierung umzusetzen. So sind nicht zu komplexe Auswertungen auf der Basis dieses Textes leicht möglich.

Regression analysis. --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- R (Computer program language). --- GNU-S (Computer program language) --- Domain-specific programming languages

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Regression analysis. --- Social sciences --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Statistical methods. --- Methods in social research (general)

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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.

Python (Computer program language) --- Regression analysis. --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Scripting languages (Computer science)