DevanshKandpal: Bot: wrap bare course codes in wikilinks

2026-04-14T16:21:15Z

Bot: wrap bare course codes in wikilinks

← Older revision		Revision as of 16:21, 14 April 2026
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	\| credits = 3		\| credits = 3
	\| credit_structure = 3-0-0		\| credit_structure = 3-0-0
	\| pre_requisites = AIL701		\| pre_requisites = [[AIL701]]
	\| overlaps = MTL712		\| overlaps = [[MTL712]]
	}}		}}

	== AIL712 : Multivariate Statistical Methods ==		== AIL712 : Multivariate Statistical Methods ==
	Introduction to multivariate data, geometry of a sample, Mahalanobis distance, mean and covariance, generalized variance, vector block distribution, linear combination of variables, multivariate Gaussian distribution and its properties, some tests of multivariate normality (like Mardia test, Shapiro-Wilk test, Henze-Zirkler test), sampling distribution and large sample behavior of mean and covariance, Wishart distribution inference about mean vector, Hotelling's T-square and likelihood ratio test, confidence region. Comparison of several multivariate populations, MANOVA, multivariate linear regression, inferences about regression models and parameters, model checking, principal component analysis, factor analysis, orthogonal factor models, factor rotation, strategy for factor analysis, canonical correlation analysis, interpreting population by canonical variables, large sample inferences.		Introduction to multivariate data, geometry of a sample, Mahalanobis distance, mean and covariance, generalized variance, vector block distribution, linear combination of variables, multivariate Gaussian distribution and its properties, some tests of multivariate normality (like Mardia test, Shapiro-Wilk test, Henze-Zirkler test), sampling distribution and large sample behavior of mean and covariance, Wishart distribution inference about mean vector, Hotelling's T-square and likelihood ratio test, confidence region. Comparison of several multivariate populations, MANOVA, multivariate linear regression, inferences about regression models and parameters, model checking, principal component analysis, factor analysis, orthogonal factor models, factor rotation, strategy for factor analysis, canonical correlation analysis, interpreting population by canonical variables, large sample inferences.

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2026-03-04T10:23:08Z

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{{Infobox Course
| code = AIL712
| name = Multivariate Statistical Methods
| credits = 3
| credit_structure = 3-0-0
| pre_requisites = AIL701
| overlaps = MTL712
}}

== AIL712 : Multivariate Statistical Methods ==
Introduction to multivariate data, geometry of a sample, Mahalanobis distance, mean and covariance, generalized variance, vector block distribution, linear combination of variables, multivariate Gaussian distribution and its properties, some tests of multivariate normality (like Mardia test, Shapiro-Wilk test, Henze-Zirkler test), sampling distribution and large sample behavior of mean and covariance, Wishart distribution inference about mean vector, Hotelling's T-square and likelihood ratio test, confidence region. Comparison of several multivariate populations, MANOVA, multivariate linear regression, inferences about regression models and parameters, model checking, principal component analysis, factor analysis, orthogonal factor models, factor rotation, strategy for factor analysis, canonical correlation analysis, interpreting population by canonical variables, large sample inferences.

AIL712 - Revision history

DevanshKandpal: Bot: wrap bare course codes in wikilinks

Prashantt492: Creating course page via bot