An introduction to regression analysis and statistical forecasting. Topics include simple and multiple linear regression, least squares estimation, inference and confidence intervals, residual analysis and model diagnostics, outliers and influential observations, variable selection and model building, polynomial and indicator variable regression, logistic regression, and time series forecasting methods including exponential smoothing and ARIMA models. Emphasis is placed on both the theoretical foundations and practical application using statistical software.