Covers regression diagnostics for linear models, emphasizing the importance of checking assumptions and identifying outliers and influential observations.
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.
Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.
Covers fundamental concepts in probability and statistics, emphasizing data analysis techniques and statistical modeling.
