This course covers the basic ideas of Applied Regression Analysis including: Simple Linear Regression; Residual Analysis; Autocorrelation; Multiple Regression; Parameter Estimation and Testing; Model Selection Procedures; Polynomial Regression; Indicator Variables; Multicollinearity; Outliers and Influential Observations. Generally, statistical regression is collection of methods for determining and using models that explain how a response variable (dependent variable) relates to one or more explanatory variables (predictor variables).

CILO-1: Be equipped with solid background with polynomial regression. CILO-2: Be equipped with solid background with simple linear regression. CILO-3: Be able to use the regression models to explain how a response variable relates to another. CILO-4: Be able to construct the regression models in real data sets.