Applied regression analysis, linear models, and related methods

著者

書誌事項

Applied regression analysis, linear models, and related methods

John Fox

Sage Publications, c1997

大学図書館所蔵 件 / 全30件

この図書・雑誌をさがす

注記

Includes bibliographical references (p. 575-581) and indexes

内容説明・目次

内容説明

An accessible, detailed and up-to-date treatment of regression analysis, linear models and closely related methods is provided in this book. Incorporating nearly 200 graphs and numerous examples and exercises that employ real data from the social sciences, the book begins with a consideration of the role of statistical data analysis in social research. It then moves on to cover the following topics: graphical methods for examining and transforming data; linear least-squares regression; dummy-variables regression; analysis of variance; diagnostic methods for discovering whether a linear model fit to data adequately represents the data; extensions to linear least squares, including logit and probit models, time-series regression, nonlinear regression, robust regression and nonparametric regression; and empirical methods for assessing sampling variation, including the bootstrap and cross-validation.

目次

PART ONE: PRELIMINARIES Statistics and Social Science What Is Regression Analysis? Examining Data Transforming Data PART TWO: LINEAR MODELS AND LEAST SQUARES Linear Least-Squares Regression Statistical Inference for Regression Dummy-Variable Regression Analysis of Variance Statistical Theory for Linear Models The Vector Geometry of Linear Models PART THREE: LINEAR-MODEL DIAGNOSTICS Unusual and Influential Data Diagnosing Nonlinearity, Nonconstant Error Variance, and Nonnormality Collinearity and Its Purported Remedies PART FOUR: BEYOND LINEAR LEAST SQUARES Extending Linear Least Squares Time Series, Nonlinear, Robust, and Nonparametric Regression Logit and Probit Models Assessing Sampling Variation Bootstrapping and Cross-Validation

「Nielsen BookData」 より

詳細情報

ページトップへ