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7.2.8 - Simultaneous (1 - α) x 100% Confidence Intervals.7.2.7 - Testing for Equality of Mean Vectors when \(Σ_1 ≠ Σ_2\).7.2.6 - Model Assumptions and Diagnostics Assumptions.7.2.4 - Bonferroni Corrected (1 - α) x 100% Confidence Intervals.7.2.2 - Upon Which Variable do the Swiss Bank Notes Differ? - Two Sample Mean Problem.7.2.1 - Profile Analysis for One Sample Hotelling's T-Square.7.1.15 - The Two-Sample Hotelling's T-Square Test Statistic.7.1.12 - Two-Sample Hotelling's T-Square.7.1.11 - Question 2: Matching Perceptions.7.1.8 - Multivariate Paired Hotelling's T-Square.7.1.7 - Question 1: The Univariate Case.7.1.4 - Example: Women’s Survey Data and Associated Confidence Intervals.7.1.1 - An Application of One-Sample Hotelling’s T-Square.Lesson 7: Inferences Regarding Multivariate Population Mean.6.2 - Example: Wechsler Adult Intelligence Scale.Lesson 6: Multivariate Conditional Distribution and Partial Correlation.5.2 - Interval Estimate of Population Mean.5.1 - Distribution of Sample Mean Vector.Lesson 5: Sample Mean Vector and Sample Correlation and Related Inference Problems.4.7 - Example: Wechsler Adult Intelligence Scale.4.6 - Geometry of the Multivariate Normal Distribution.4.4 - Multivariate Normality and Outliers.4.3 - Exponent of Multivariate Normal Distribution.Lesson 4: Multivariate Normal Distribution.Lesson 3: Graphical Display of Multivariate Data.Lesson 2: Linear Combinations of Random Variables.1.5 - Additional Measures of Dispersion.

Lesson 1: Measures of Central Tendency, Dispersion and Association.

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In particular we will consider the computation of the eigenvalues and eigenvectors of a symmetric matrix \(\textbf\lambda_j = \lambda_1 \times \lambda_2 \times \dots \times \lambda_p\) To do this we first must define the eigenvalues and the eigenvectors of a matrix. The next thing that we would like to be able to do is to describe the shape of this ellipse mathematically so that we can understand how the data are distributed in multiple dimensions under a multivariate normal.