Hypothesis Testing and Non-Parametric Regression

Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.

Covers nonparametric regression, scatterplot smoothing, kernel methods, and bias-variance tradeoff.

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

Covers confidence intervals, R2, and examples on cement heat evolution and car horsepower-MPG relationships.

Explores non-parametric regression techniques, including splines, bias-variance tradeoff, orthogonal functions, wavelets, and modulation estimators.