Statistical Modeling is the process of creating mathematical representations of real-world phenomena using statistical techniques to understand relationships between variables, make predictions, and test theories.

Types of statistical models include:

• Linear Models: Linear regression, multiple regression, ANOVA
• Generalized Linear Models: Logistic regression, Poisson regression
• Time Series Models: ARIMA, exponential smoothing, seasonal decomposition
• Survival Models: Cox proportional hazards, Kaplan-Meier estimation
• Bayesian Models: Incorporating prior knowledge and uncertainty

Statistical modeling involves model specification, parameter estimation, model validation, and interpretation. It differs from machine learning by emphasizing interpretability, hypothesis testing, and understanding causal relationships rather than solely optimizing predictive performance.