Explores estimators, bias, and efficiency in statistics, emphasizing the trade-off between bias and variability.
Explores replicates, visualization methods, central tendency measures, outliers, dispersion, averages, residuals, and unbiased estimators.
Covers error estimation in Latin Hypercube Sampling, emphasizing the importance of accurate variance estimation.
Covers confidence intervals, hypothesis tests, standard errors, statistical models, likelihood, Bayesian inference, ROC curve, Pearson statistic, goodness of fit tests, and power of tests.
Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.
