Review of simple and multiple regression with matrices, Gauss-Markov theorem, polynomial regression, indicator variables, residual analysis, weighted least squares, variable selection techniques, nonlinear regression, correlation analysis and autocorrelation. Computer packages are used for statistical analyses. Includes: Experiential Learning Activity Precludes additional credit for STAT 3553. Prerequisite(s): i) STAT 2509 or STAT 2602 or STAT 2607 or ECON 2202 or equivalent; and ii) MATH 1152 or MATH 1107 or MATH 1119 or equivalent; or permission of the School. Lectures three hours a week and one hour laboratory.
Review of simple and multiple regression with matrices, Gauss-Markov theorem, polynomial regression, indicator variables, residual analysis, weighted least squares, variable selection techniques, nonlinear regression, correlation analysis and autocorrelation. Computer packages are used for statistical analyses. Includes: Experiential Learning Activity Precludes additional credit for STAT 3553. Prerequisite(s): i) STAT 2509 or STAT 2602 or STAT 2607 or ECON 2202 or equivalent; and ii) MATH 1152 or MATH 1107 or MATH 1119 or equivalent; or permission of the School. Lectures three hours a week and one hour laboratory.
