STAT 4123 Course Outline

STAT 4123: Applied Statistics I

Course Outline

Week 1: Simple linear regression; least-squares estimators for the regression coefficients Week 2: Properties of the least-squares estimators; estimation of the error variance term; ANOVA table; hypothesis testing on the regression coefficients Week 3: Confidence intervals of the regression coefficients and the mean response Week 4: No-intercept model; multiple linear regression; introducing matrix notation and properties of the least squares estimators Week 5: Review; Midterm 1 Week 6: Hypothesis testing in multiple linear regression (Overall F test, individual t-tests, tests on subsets of the coefficients, general linear hypothesis); ANOVA table Week 7: Confidence intervals in multiple linear regression; extrapolation Week 8: Model adequacy checking; residual analysis Week 9: Detection of outliers; variance stabilizing transformations Week 10: More on transformations; Box-Cox method; weighted least-squares method Week 11: Review; Midterm 2 Week 12: Diagnostics for leverage and influence Week 13: Multicollinearity (causes, effects, methods of detecting, methods for dealing with the problems) Week 14: Polynomial regression in one variable; polynomial regression with more variables; introduction of interaction terms Week 15: Indicator variables; categorical predictors in the presence of other predictors; interaction terms Week 16: Review; Final Exam