Linear Models and Their Applications

3 credits · Undergraduate Programs

Multiple linear regression model, parameter estimation, simultaneous confidence intervals and general linear hypothesis testing. Residual analysis and outliers. Model selection: best regression, stepwise regression algorithms. Transformation of variables and non-linear regression. Applications to forecasting. Variable selection in high-dimensional data using linear regression. Computer analysis of practical real world data. Prerequisite(s): Statistics 323 or Data Science 305; and Mathematics 211.