MANCOVA for one way classification with homogeneity of regression coefficient vectors

• Published in: IOP conference series. Materials Science and Engineering Vol. 263; no. 4; pp. 42134 - 42138
• Main Authors: Mokesh Rayalu, G, Ravisankar, J, Mythili, G Y
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.11.2017
• Subjects: Dependent variables; Homogeneity; Multivariate analysis; Normal distribution; Regression coefficients; Statistical analysis; Statistical methods; Statistical models
• ISSN: 1757-8981; 1757-899X; 1757-899X
• DOI: 10.1088/1757-899X/263/4/042134

The MANOVA and MANCOVA are the extensions of the univariate ANOVA and ANCOVA techniques to multidimensional or vector valued observations. The assumption of a Gaussian distribution has been replaced with the Multivariate Gaussian distribution for the vectors data and residual term variables in the statistical models of these techniques. The objective of MANCOVA is to determine if there are statistically reliable mean differences that can be demonstrated between groups later modifying the newly created variable. When randomization assignment of samples or subjects to groups is not possible, multivariate analysis of covariance (MANCOVA) provides statistical matching of groups by adjusting dependent variables as if all subjects scored the same on the covariates. In this research article, an extension has been made to the MANCOVA technique with more number of covariates and homogeneity of regression coefficient vectors is also tested.