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Multivariate Normal Distribution as an approximation to the Multinomial Distribution
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Classroom Contents
Multivariate
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- 1 A Simple Multivariate Test for One Sided Alternatives
- 2 The Multivariate Sign Test
- 3 Probability of 1st Quadrant for a Scaled Bivariate Normal Random Variable
- 4 Using R: Calculating Probability for a Bivariate Normal Random Variable
- 5 Using R: The Multivariate Sign Test
- 6 Power and Sample Size in R: Multivariate Sign Test
- 7 Statistical Distance
- 8 A Square-Root Matrix
- 9 Extended Cauchy-Schwarz Inequality
- 10 Linear Discriminant Analysis
- 11 Distribution of Quadratic Forms (part 1)
- 12 Distribution of Quadratic Forms (part 2)
- 13 Distribution of Quadratic Forms (part 3)
- 14 Gaussian Integrals
- 15 Spherical Coordinates
- 16 Multivariate Normal Random Variable transformed to a Multivariate Uniform Random Variable
- 17 Rotational Invariance
- 18 (1-a)% Confidence Region for a multivariate mean vector when the data are multivariate normal
- 19 Multivariate Descriptive Statistics
- 20 Multivariate Normal Distribution as an approximation to the Multinomial Distribution
- 21 Testing all Treatments Arms against a Control Arm using Follmann's Test
- 22 Dose Escalation Hypotheses Testing using Follmann's Test
- 23 Using R to test multivariate ordered alternatives with Follmann's test
- 24 Cov(y1, y2)=0 if and only if (y1 independent of y2)
- 25 Random Vectors and Random Matrices
- 26 Principal Components (part 1): Background
- 27 Principal Components (part 2): Derivation
- 28 Principal Components (part 3): "Explained" Variance
- 29 Principal Components (part 4): Correlation
