Semester 1, 2018

STAT2008 Notes

20 pages

11,220 words




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.


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




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February 2017