COVAR: Computer Program for Multifactor Relative Risks and Tests of Hypotheses Using a Variance-Covariance Matrix from Linear and Log-Linear Regression

COVAR: Computer Program for Multifactor Relative Risks and Tests of Hypotheses Using a Variance-Covariance Matrix from Linear and Log-Linear Regression

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A computer program for multifactor relative risks, confidence limits, and tests of hypotheses using regression coefficients and a variance-covariance matrix obtained from a previous additive or multiplicative regression analysis is described in detail. Data used by the program can be stored and inpu...

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Bibliographic Details
Main Author: Leif E. Peterson
Format: Article
Language:English
Published: Foundation for Open Access Statistics 1997-11-01
Series:Journal of Statistical Software
Online Access:http://www.jstatsoft.org/index.php/jss/article/view/1552
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spelling doaj-29d4fccc55c84fbbba76bcbce308a2232020-11-24T21:39:27ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76601997-11-012112110.18637/jss.v002.i04156COVAR: Computer Program for Multifactor Relative Risks and Tests of Hypotheses Using a Variance-Covariance Matrix from Linear and Log-Linear RegressionLeif E. PetersonA computer program for multifactor relative risks, confidence limits, and tests of hypotheses using regression coefficients and a variance-covariance matrix obtained from a previous additive or multiplicative regression analysis is described in detail. Data used by the program can be stored and input from an external disk-file or entered via the keyboard. The output contains a list of the input data, point estimates of single or joint effects, confidence intervals and tests of hypotheses based on a minimum modified chi-square statistic. Availability of the program is also discussed.http://www.jstatsoft.org/index.php/jss/article/view/1552
collection DOAJ
language English
format Article
sources DOAJ
author Leif E. Peterson
spellingShingle Leif E. Peterson
COVAR: Computer Program for Multifactor Relative Risks and Tests of Hypotheses Using a Variance-Covariance Matrix from Linear and Log-Linear Regression
Journal of Statistical Software
author_facet Leif E. Peterson
author_sort Leif E. Peterson
title COVAR: Computer Program for Multifactor Relative Risks and Tests of Hypotheses Using a Variance-Covariance Matrix from Linear and Log-Linear Regression
title_short COVAR: Computer Program for Multifactor Relative Risks and Tests of Hypotheses Using a Variance-Covariance Matrix from Linear and Log-Linear Regression
title_full COVAR: Computer Program for Multifactor Relative Risks and Tests of Hypotheses Using a Variance-Covariance Matrix from Linear and Log-Linear Regression
title_fullStr COVAR: Computer Program for Multifactor Relative Risks and Tests of Hypotheses Using a Variance-Covariance Matrix from Linear and Log-Linear Regression
title_full_unstemmed COVAR: Computer Program for Multifactor Relative Risks and Tests of Hypotheses Using a Variance-Covariance Matrix from Linear and Log-Linear Regression
title_sort covar: computer program for multifactor relative risks and tests of hypotheses using a variance-covariance matrix from linear and log-linear regression
publisher Foundation for Open Access Statistics
series Journal of Statistical Software
issn 1548-7660
publishDate 1997-11-01
description A computer program for multifactor relative risks, confidence limits, and tests of hypotheses using regression coefficients and a variance-covariance matrix obtained from a previous additive or multiplicative regression analysis is described in detail. Data used by the program can be stored and input from an external disk-file or entered via the keyboard. The output contains a list of the input data, point estimates of single or joint effects, confidence intervals and tests of hypotheses based on a minimum modified chi-square statistic. Availability of the program is also discussed.
url http://www.jstatsoft.org/index.php/jss/article/view/1552
work_keys_str_mv AT leifepeterson covarcomputerprogramformultifactorrelativerisksandtestsofhypothesesusingavariancecovariancematrixfromlinearandloglinearregression
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