STAT 429 - APPLIED REGRESSION ANALYSIS - Fall 2005
Multiple linear regression model including parameter estimation, simultaneous confidence intervals and general linear hypothesis testing using matrix algebra. Applications to forecasting. Residual analysis and outliers. Model selection: best regression, stepwise regression algorithms. Transformation of variables and non-linear regression. Computer analysis of practical real world data. This course may not be repeated for credit.
Hours
H(3-1T)
Prerequisite(s)
Mathematics 323
Antirequisite(s)
Credit for both Statistics 429 and 431 will not be allowed