This course provides a comprehensive exploration of statistical modeling techniques, including simple linear regression (SLR), multiple linear regression (MLR), and time series analysis. In SLR, students learn about the SLR model, least square estimation of model parameters, interpretation of parameter estimates, R-squared, the normal model, distribution of parameter estimates, confidence intervals for model parameters, hypothesis testing, and the interplay between confidence intervals and hypothesis tests. Moving on to MLR, students delve into the estimation of model parameters, the Hat matrix, distribution of parameter estimates, confidence intervals and hypothesis tests for individual parameters, confidence and prediction intervals for the response variable, handling multicollinearity, modeling categorical variables, transforming the response variable, examining interactions, residual analysis, and techniques for model selection. Additionally, students are introduced to time series analysis, covering linear regression modeling for time series data, seasonal and trend components, stationarity, autocorrelation function, AR(1) and MA(1) models, ARMA models, ARIMA models, and ARCH/GARCH models for volatility modeling. By the end of the course, students will have acquired the ability to apply these methods to real-world problems, evaluate the best model for time-series data, and interpret results effectively.