STAT2008 Notes

Description

Notes include weeks 1-12 lectures and include notes from the assigned readings of the textbook. Relevant formulas are highlighted in yellow. Tutorial material is included where appropriate. Topics: Week 1: Introduction. Getting started with R. Simple Linear Regression (revision).Parameter interpretation/estimation Week 2: Matrix approach to linear regression. Properties of least squares estimators. Week 3: ANOVA. Hypothesis testing and interval estimation in a SLR context Week 4: Prediction intervals. Regression diagnostics (residual plots). Week 5: Outliers and influential observations. Scale transformations. Week 6: Introduction to Multiple Regression. Model interpretation and estimation. Week 7: Model interpretation continued (discussion of causality) Week 8: ANOVA for multiple regression. Sequential sum of squares. Week 9: Hypothesis testing, confidence intervals and prediction for multiple regression Week 10: Model diagnostics. Outlier detection. Types of residuals. Influence diagnostics. Multicollinearity. Week 11: Model selection and criteria for comparing models. Week 12: Review/Additional material