Multivariate Statistics: Normal Distribution

Related lectures (31)

Page 3 of 4

Explores variance, covariance, and practical applications in statistics and probability.

Explores covariance, statistical dependence, education-fertility relationship, hypothesis testing, and comparison statistics for continuous outcomes.

Explores Principal Component Analysis theory, properties, applications, and hypothesis testing in multivariate statistics.

Discusses probability distributions and the Central Limit Theorem, emphasizing their importance in data science and statistical analysis.

Introduces inferential statistics, covering sampling, central tendency, dispersion, histograms, z-scores, and the normal distribution.

Covers random vectors, joint probability, and conditional probability in communication stochastic models.

Covers random vectors, joint distribution, conditional density functions, independence, covariance, correlation, and conditional expectation.

Covers elliptical distributions, including properties, applications, and risk management implications.

Covers the Bayes discriminant rule for allocating individuals to populations based on measurements and prior probabilities.

Covers continuous random variables, probability density functions, and distributions, with practical examples.