We're still actively developing this site. If you encounter any issues, please report them! - Report an issue

MULTIVARIATE ANALYSIS I

STAT 760
Course Description

Multivariate normal distribution, estimation of mean and covariance matrix; Wishart distribution; distribution of partial and multiple correlation coefficients; Hotelling's T-squared, principal components.

Prerequisties
Satisfies

This course does not satisfy any prerequisites.

Credits

Not Reported

Offered

Not Reported

Grade Point Average
Not Reported

Could not calculate change

Completion Rate
Not Reported

Could not calculate change

A Rate
Not Reported

Could not calculate change

Class Size
Not Reported

-100% from Historical

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

Similar Courses

THEORY AND APPLICATION OF REGRESSION AND ANALYSIS OF VARIANCE I

STAT 849

Theory and applications of the general linear model; graphical methods; simultaneous inference; regression diagnostics; analysis of variance of fixed, random and mixed effects models; ANCOVA: violations of assumptions.

INTRODUCTION TO PROBABILITY AND MATHEMATICAL STATISTICS I

MATH/STAT 309

Probability and combinatorial methods, discrete and continuous, univariate and multivariate distributions, expected values, moments, normal distribution and derived distributions, estimation.

THEORY AND APPLICATION OF REGRESSION AND ANALYSIS OF VARIANCE II

STAT 850

Theory and applications of the general linear model; graphical methods; simultaneous inference; regression diagnostics; analysis of variance of fixed, random and mixed effects models; ANCOVA: violations of assumptions.

APPLIED MULTIVARIATE ANALYSIS

STAT 456

Theory and applications of multivariate statistical methods. Basic concepts and statistical reasoning which underlie the techniques of multivariate analysis. Ideas rather than derivations stressed although basic models discussed to give the student some feeling for their adequacy in particular situations. Acquaintance with and use of existing computer programs in the multivariate analysis area.

DECISION TREES FOR MULTIVARIATE ANALYSIS

STAT 761

Tree construction, including finding splits, tree-pruning and error estimation. Categorical predictor variables, missing or censored data, prior class-probabilities, and unequal misclassification costs. Selection bias. Comparison with other statistics and machine-learning methods. Extensions to piecewise linear and non-least squares regression models.