Continuous Distributions: Testing and Estimation

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Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.

Explores common probability distributions, special distributions, and entropy concepts.

Introduces descriptive statistics, hypothesis testing, p-values, and confidence intervals, emphasizing their importance in data analysis.

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Covers the construction of confidence intervals for a normal distribution with unknown mean and variance.

Covers the normal distribution, including its properties and calculations.

Introduces continuous random variables and their probability distributions, emphasizing their applications in statistics and data science.

Explores the distribution theory of least squares estimators in a Gaussian linear model.

Explores various continuous probability distributions and expectation of random variables.

Covers hypothesis testing, confidence intervals, data distributions, and statistical significance in data analysis.