Schedule

Schedule for Math 158, Linear Models, Spring 2022

Here’s your roadmap for the semester! Each week, follow the general process outlined below:

• Enjoy the notes / readings

• Attend class, review slides for anything you missed on the agenda, review the warm-up if you have any questions after completing it during class.

• Complete the assignment (see Sakai messages for HW link)

• Discuss the reflection questions and ethics considerations (see class notes) with your classmates, mentor, and professor

• ALSM: Applied Linear Statistical Models, 5th ed., Kutner, Nachtsheim, Neter, Li. You should be able to find online.

• ISLR: An Introduction to Statistical Learning, 2nd ed., James, Witten, Hastie, Tibshirani. https://www.statlearning.com/

  • ISLR Labs using tidymodels.

date	topic	agenda	readings	in class	article
Week 1 1.18.22	starting + R + RStudio + Git + GitHub + SLR	\(\boldsymbol{\cdot}\)course info \(\boldsymbol{\cdot}\)R + Git \(\boldsymbol{\cdot}\)what is a linear model? \(\boldsymbol{\cdot}\)least squares \(\boldsymbol{\cdot}\)norm errors \(\boldsymbol{\cdot}\)tech cond	Introduction Git + GitHub ALSM 1 ISLR 3.1	Git + Tidy WU 1 WU 2 least sq	happygitwithR Why Git? + monsters
Week 2 1.25.22	Inference	\(\boldsymbol{\cdot}\)Inf on \(\beta_1\) \(\boldsymbol{\cdot}\)CI for \(\beta_1\) \(\boldsymbol{\cdot}\)CI for mean \(\boldsymbol{\cdot}\)PI for indiv \(\boldsymbol{\cdot}\) correlation	SLR ALSM 2	WU 3 dist \(\beta_1\)
Week 3 2.1.22	Diagnositcs	\(\boldsymbol{\cdot}\) \(R^2\) \(\boldsymbol{\cdot}\) F test \(\boldsymbol{\cdot}\) residual plots	Diagnostics I ALSM 3	WU 4 WU 5
Week 4 2.8.22	Simultaneous Inference + Matrices	\(\boldsymbol{\cdot}\) \(\beta_0\) & \(\beta_1\) \(\boldsymbol{\cdot}\) linear algebra	Simult Inf Matrices ALSM 4 & 5	WU 6 WU 7	Peer review
2.8.22	Proj 1: Data	Proj 1: Data
Week 5 2.15.22	MLR	\(\boldsymbol{\cdot}\) indicator terms \(\boldsymbol{\cdot}\) interaction terms \(\boldsymbol{\cdot}\) quadratic terms \(\boldsymbol{\cdot}\) F test \(\boldsymbol{\cdot}\) \(R^2\) & \(R^2_{adj}\) \(\boldsymbol{\cdot}\) combos of coefs	MLR ALSM 6 ISLR 3.2	WU 8 WU 9	Criticism
2.20.22	Proj 2: SLR	Proj 2: SLR
Week 6 2.22.22	catch-up	\(\boldsymbol{\cdot}\) see Sakai for study materials
2.24.22	Exam 1
Week 7 3.1.22	Feature Engineering & CV	\(\boldsymbol{\cdot}\) tidymodels \(\boldsymbol{\cdot}\) feature engineering \(\boldsymbol{\cdot}\) cross validation	Process ISLR 5.1	Feature Engineering Cross Validation An Example WU 10 WU 11	Variable meaning
3.3.22	Peer 2: SLR	Peer Review: SLR on GitHub
Week 8 3.8.22	Model Building	\(\boldsymbol{\cdot}\) sums of squares \(\boldsymbol{\cdot}\) nested F-tests \(\boldsymbol{\cdot}\) coef of determination	Build ALSM 7	WU 12 WU 13
3.15.22	Spring Break
Week 9 3.22.22	Model Building	\(\boldsymbol{\cdot}\) mulicollinearity \(\boldsymbol{\cdot}\) stepwise procedures	Build ALSM 9 ISLR 6.1	Model Strategy WU 14 WU 15	Compassion + Models
Week 10 3.29.22	Diagnostics	\(\boldsymbol{\cdot}\) residuals \(\boldsymbol{\cdot}\) leverage \(\boldsymbol{\cdot}\) influence	Diagnostics II ALSM 10	Outliers WU 16 WU 17
Week 11 4.5.22	Ridge Regression	\(\boldsymbol{\cdot}\) ridge regression	Ridge ALSM 11.2 ISLR 6.2	Ridge Regression WU 18 WU 19
4.5.22	Proj 3: MLR	Proj 3: MLR
Week 12 4.12.22	catch-up	\(\boldsymbol{\cdot}\) see Sakai for study materials
4.14.22	Exam 2
Week 13 4.19.22	Lasso	\(\boldsymbol{\cdot}\) Lasso \(\boldsymbol{\cdot}\) elastic net \(\boldsymbol{\cdot}\) step functions	Lasso ISLR 6.2	WU 20 WU 21
Week 14 4.26.22	Smoothers	\(\boldsymbol{\cdot}\) ridge regression \(\boldsymbol{\cdot}\) loess	Smooth ISLR 7	WU 22	Smoothing as average
5.3.22	Exam 3
5.10.22	Proj 4: Beyond Linear + Summary	Proj 4: Fin