Understanding Regression Analysis [electronic resource] /

Understanding Regression Analysis [electronic resource] /

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By assuming it is possible to understand regression analysis without fully comprehending all its underlying proofs and theories, this introduction to the widely used statistical technique is accessible to readers who may have only a rudimentary knowledge of mathematics. Chapters discuss: -descriptive statistics using vector notation and the components of a simple regression model; -the logic of sampling distributions and simple hypothesis testing; -the basic operations of matrix algebra and the properties of the multiple regression model; -testing compound hypotheses and the application of the regression model to the analyses of variance and covariance, and -structural equation models and influence statistics.

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Bibliographic Details
Main Authors: Allen, Michael Patrick. author., SpringerLink (Online service)
Format: Texto biblioteca
Language:eng
Published: Boston, MA : Springer US, 1997
Subjects:Social sciences., Public health., Social Sciences., Social Sciences, general., Public Health.,
Online Access:http://dx.doi.org/10.1007/b102242
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spelling KOHA-OAI-TEST:2104442018-07-30T23:42:19ZUnderstanding Regression Analysis [electronic resource] / Allen, Michael Patrick. author. SpringerLink (Online service) textBoston, MA : Springer US,1997.engBy assuming it is possible to understand regression analysis without fully comprehending all its underlying proofs and theories, this introduction to the widely used statistical technique is accessible to readers who may have only a rudimentary knowledge of mathematics. Chapters discuss: -descriptive statistics using vector notation and the components of a simple regression model; -the logic of sampling distributions and simple hypothesis testing; -the basic operations of matrix algebra and the properties of the multiple regression model; -testing compound hypotheses and the application of the regression model to the analyses of variance and covariance, and -structural equation models and influence statistics.The origins and uses of regression analysis -- Basic matrix algebra: Manipulating vectors -- The mean and variance of a variable -- Regression models and linear functions -- Errors of prediction and least-squares estimation -- Least-squares regression and covariance -- Covariance and linear independence -- Separating explained and error variance -- Transforming variables to standard form -- Regression analysis with standardized variables -- Populations, samples, and sampling distributions -- Sampling distributions and test statistics -- Testing hypotheses using the t test -- The t test for the simple regression coefficient -- More matrix algebra: Manipulating matrices -- The multiple regression model -- Normal equations and partial regression coefficients -- Partial regression and residualized variables -- The coefficient of determination in multiple regression -- Standard errors of partial regression coefficients -- The incremental contributions of variables -- Testing simple hypotheses using the F test -- Testing compound hypotheses using the F test -- Testing hypotheses in nested regression models -- Testing for interaction in multiple regression -- Nonlinear relationships and variable transformations -- Regression analysis with dummy variables -- One-way analysis of variance using the regression model -- Two-way analysis of variance using the regression model -- Testing for interaction in analysis of variance -- Analysis of covariance using the regression model -- Interpreting interaction in analysis of covariance -- Structural equation models and path analysis -- Computing direct and total effects of variables -- Model specification in regression analysis -- Influential cases in regression analysis -- The problem of multicollinearity -- Assumptions of ordinary least-squares estimation -- Beyond ordinary regression analysis.By assuming it is possible to understand regression analysis without fully comprehending all its underlying proofs and theories, this introduction to the widely used statistical technique is accessible to readers who may have only a rudimentary knowledge of mathematics. Chapters discuss: -descriptive statistics using vector notation and the components of a simple regression model; -the logic of sampling distributions and simple hypothesis testing; -the basic operations of matrix algebra and the properties of the multiple regression model; -testing compound hypotheses and the application of the regression model to the analyses of variance and covariance, and -structural equation models and influence statistics.Social sciences.Public health.Social Sciences.Social Sciences, general.Public Health.Springer eBookshttp://dx.doi.org/10.1007/b102242URN:ISBN:9780585256573
institution COLPOS
collection Koha
country México
countrycode MX
component Bibliográfico
access En linea
En linea
databasecode cat-colpos
tag biblioteca
region America del Norte
libraryname Departamento de documentación y biblioteca de COLPOS
language eng
topic Social sciences.
Public health.
Social Sciences.
Social Sciences, general.
Public Health.
Social sciences.
Public health.
Social Sciences.
Social Sciences, general.
Public Health.
spellingShingle Social sciences.
Public health.
Social Sciences.
Social Sciences, general.
Public Health.
Social sciences.
Public health.
Social Sciences.
Social Sciences, general.
Public Health.
Allen, Michael Patrick. author.
SpringerLink (Online service)
Understanding Regression Analysis [electronic resource] /
description By assuming it is possible to understand regression analysis without fully comprehending all its underlying proofs and theories, this introduction to the widely used statistical technique is accessible to readers who may have only a rudimentary knowledge of mathematics. Chapters discuss: -descriptive statistics using vector notation and the components of a simple regression model; -the logic of sampling distributions and simple hypothesis testing; -the basic operations of matrix algebra and the properties of the multiple regression model; -testing compound hypotheses and the application of the regression model to the analyses of variance and covariance, and -structural equation models and influence statistics.
format Texto
topic_facet Social sciences.
Public health.
Social Sciences.
Social Sciences, general.
Public Health.
author Allen, Michael Patrick. author.
SpringerLink (Online service)
author_facet Allen, Michael Patrick. author.
SpringerLink (Online service)
author_sort Allen, Michael Patrick. author.
title Understanding Regression Analysis [electronic resource] /
title_short Understanding Regression Analysis [electronic resource] /
title_full Understanding Regression Analysis [electronic resource] /
title_fullStr Understanding Regression Analysis [electronic resource] /
title_full_unstemmed Understanding Regression Analysis [electronic resource] /
title_sort understanding regression analysis [electronic resource] /
publisher Boston, MA : Springer US,
publishDate 1997
url http://dx.doi.org/10.1007/b102242
work_keys_str_mv AT allenmichaelpatrickauthor understandingregressionanalysiselectronicresource
AT springerlinkonlineservice understandingregressionanalysiselectronicresource
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