Add to ORKGA covariance analysis procedure which compares multiple linear regression equations is developed by extending the general linear hypothesis model of full rank to encompass heterogeneous data. A FORTRAN IV computer program tests parallelism and coincidence amongst sets of regression equations. By a practical example both the theory and the computer program are demonstrated.Graduate and Postdoctoral StudiesGraduat
Much of the research on multiple comparison and simultaneous inference in the past sixty years or so...
. A general linear model can be written as Y = XB 0 + U , where Y is an N \Theta p matrix of obser...
summary:In regular multivariate regression model a test of linear hypothesis is dependent on a struc...
The concepts of analysis of covariance are reviewed by emphasizing that it is just a combination of ...
The aim of this study is to introduce the reader to the topic of Multiple Linear Regression Analysis...
Hypothetical data sets are used to demonstrate how canonical correlation methods subsume other commo...
In a multiple-regression analysis it is often necessary to assign a value, different from the least-...
A computer program for multifactor relative risks, confidence limits, and tests of hypotheses using ...
Abstract: Multivariate approach to generate variance covariance and partial correlation coefficients...
This chapter provides a brief and basic introduction to regression techniques and to the use of seve...
Canonical Correlation Analysis (CCA) can be conceptualized as a multivariate regression involving mu...
Multivariate multiple linear regression is multiple linear regression, but with multiple responses. ...
This article takes correlation coefficients as the starting point to obtain inferential results in l...
Multiple linear regression is a commonly used procedure for testing the fit of experimental data to ...
Abstract Abstract Multivariate multiple linear regression is multiple linear regression, but with mu...
Much of the research on multiple comparison and simultaneous inference in the past sixty years or so...
. A general linear model can be written as Y = XB 0 + U , where Y is an N \Theta p matrix of obser...
summary:In regular multivariate regression model a test of linear hypothesis is dependent on a struc...
The concepts of analysis of covariance are reviewed by emphasizing that it is just a combination of ...
The aim of this study is to introduce the reader to the topic of Multiple Linear Regression Analysis...
Hypothetical data sets are used to demonstrate how canonical correlation methods subsume other commo...
In a multiple-regression analysis it is often necessary to assign a value, different from the least-...
A computer program for multifactor relative risks, confidence limits, and tests of hypotheses using ...
Abstract: Multivariate approach to generate variance covariance and partial correlation coefficients...
This chapter provides a brief and basic introduction to regression techniques and to the use of seve...
Canonical Correlation Analysis (CCA) can be conceptualized as a multivariate regression involving mu...
Multivariate multiple linear regression is multiple linear regression, but with multiple responses. ...
This article takes correlation coefficients as the starting point to obtain inferential results in l...
Multiple linear regression is a commonly used procedure for testing the fit of experimental data to ...
Abstract Abstract Multivariate multiple linear regression is multiple linear regression, but with mu...
Much of the research on multiple comparison and simultaneous inference in the past sixty years or so...
. A general linear model can be written as Y = XB 0 + U , where Y is an N \Theta p matrix of obser...
summary:In regular multivariate regression model a test of linear hypothesis is dependent on a struc...