Jacamar Data Analysis

Related lectures (31)

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Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.

Covers binary classification, hypothesis testing, likelihood ratio tests, and decision rules.

Explores linear regression estimation, hypothesis testing, and practical applications in statistics.

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

Explores linear regression analysis of ozone data using statistical models.

Explores the impact of a binary covariate on binary responses using 2x2 tables.

Explores independence and conditional probability in probability and statistics, with examples illustrating the concepts and practical applications.

Covers logistic regression, likelihood function, Newton's method, and classification error estimation.

Covers fundamental concepts of probability and statistics, focusing on data analysis, graphical representation, and practical applications.