Data Cleaning, Outlier Analysis, and Distributional Robustness

Represent, summarize, and interpret univariate datasets using graphical representations, measures of central tendency, position, and dispersion.

Identify anomalies, clean missing values, and evaluate the effect of extreme values on distribution metrics and reporting choices.

Fit continuous normal distributions to empirical data, compute probabilities using Z-scores, and diagnose model validity.

Summarize bivariate categorical data, calculate joint, marginal, and conditional relative frequencies, and test for independence.

Fit linear, quadratic, and exponential models to bivariate data, evaluate fit via residuals and correlation, and distinguish correlation from causation.