Jacamar Data Analysis

Related lectures (31)

Page 3 of 4

Covers the theory of Poisson processes and the estimation of the intensity parameter R.

Covers the concepts of Demystification, Estimation, and Bayesian Inference in the context of Bayesian statistics.

Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.

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

Introduces statistics basics, including data analysis and probability theory, emphasizing central tendency, dispersion, and distribution shapes.

Explores likelihood in probability, maximum likelihood estimation, and its applications in statistical inference.

Introduces probability, estimation methods, linear models, testing, and advanced regression techniques.

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

Explores conditional density, expectations, and independence of random variables with practical examples.

Explores logistic regression model interpretation, parameter estimation, and model comparison using likelihood ratio tests.