Bias, Variance, Consistency, EMV

Explores the Method of Moments, Bias-Variance tradeoff, Consistency, Plug-In Principle, and Likelihood Principle in point estimation.

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Explores statistical models, parameter estimation, and sampling distributions in probability and statistics.

Introduces Fisher Information for parameter estimation based on collected data.