Schedule and Course Materials

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Unit	Date	Topic	Class prep	Class materials	Assignment due
0	8.20	Bootcamp: Exploring data in R	Install R/RStudio or use the Duke R container
1	8.26	Introduction	None
	8.28	Probability	Intuitive Introductory Statistics (IIS) Section 4.2 (4.1 also recommended)
	9.2	Probability Distributions	IIS 4.3 intro and 4.3.1, 4.5, 4.7 intro and 4.7.1
	9.4	Sampling Distributions & CLT	5 up to 5.1.1
	9.7				HW1 due 9.7 11:59 PM Open the file to view questions, download template to complete, render to pdf and submit on gradescope       Key:
	9.9	Maximum Likelihood Estimation	Maximum Likelihood Estimation video
	9.11	Confidence Intervals/Bootstrap	Introduction to Modern Statistics (IMS) Ch 12 up to 12.4
	9.14				First statistics reflection
	9.16	Inference I	IMS Chapter 11 (13 could also be useful)
	9.18	Inference II	IMS Chapter 20 sections 20.3 and 20.4
	9.21				HW2 due 9.21 11:59 PM Open the file to view questions, download template to complete, render to pdf and submit on gradescope      Key:
2	9.23	Simple Linear Regression	Introduction to Statistical Learning with Applications in R (ISLR) 2nd edition: Chapter 3 up to 3.1.3	Quiz 1 in class
	9.25	Multiple Linear Regression	3 videos: Multiple Linear Regression, MLR Inference , and MLR estimation and matrix notation Slides for reference: First two videos, Third video Optional/supplementary reading: ISLR 3.2 up to "deciding on important variables"
	9.30	Categorical Predictors & Interaction Terms	2 videos: Categorical Predictors, Interaction Terms Slides for reference: Categorical Predictors, Interaction Terms Optional/supplementary reading: ISLR section 3.3 up to “Nonlinear Relationships”
	10.2	Assessment & Assumptions	2 videos: Assessing the Model, Regression Assumptions Slides for reference: Assessment, Assumptions Optional/supplementary reading: ISLR section 3.2.2 section “Three: Model Fit”; 3.3.3 #1-3 (Non-linearity, correlation of error terms, non-constant variance)	Notes continued from Tuesday, but here's the filled-in version, including answers to the exercise
	10.7	Problems that can arise	2 videos: Influential Points, Multicollinearity Slides for reference: Influential points, Multicollinearity Optional/supplementary reading: ISLR section 3.3.3 #4-6
	10.8				HW3 Part 1 due 10.8 11:59 PM Open the file to view questions, download template to complete, render to pdf and submit on gradescope      Key:
	10.9	Model Selection	First read ISLR 3.2.2 part "Two: Deciding on Important Variables" Then, read Step Away from Stepwise by Gary Smith. Focus on the introduction and the conclusion. After reading, you should be able to describe in general terms why Smith argues against using stepwise variable selection, particularly with large datasets.
	10.14	NO CLASS
	10.16	NO CLASS
	10.17				HW3 Part 2 due 10.17 11:59 PM Open the file to view questions, download template to complete, render to pdf and submit on gradescope
	10.21	Review for midterm	Midterm study materials: Midterm topic list, Relevant Wooclap questions, and Last year's exam  Key:
	10.23	Midterm exam
3	10.28	Intro to GLMs	2 videos: Intro to GLMs, Odds and Odds Ratios Slides for reference: Intro to GLMs, Odds and Odds Ratios Optional/supplementary reading: ISLR 4.2, 4.3 intro and 4.3.1
	10.30	Logistic Regression Estimation	1 required video: Logistic Regression, Supplementary videos and reading: Maximum Likelihood vs Least Squares, Logistic Basics, Logistic Regression: Maximum Likelihood, Slides for reference: Logistic Regression Optional/supplementary reading: ISLR 4.3.2-4.3.4
	11.2				Second statistics reflection
	11.4	Logistic Assessment	ISLR doesn't explicitly cover metrics for logistic regression, but section 4.4.2 describes some of the same concepts (confusion matrix, sensitivity and specificity, ROC curve). Start on page 148. We will discuss these concepts with an example in class. 2 optional videos: Logistic diagnostics, Assessing models with predictions Slides for reference: Logistic diagnostics, Assessing models with prediction
	11.6	Multinomial Regression	1 video: Multinomial Regression Slides for reference: Multinomial regression Supplemental reading: ISLR briefly covers multinomial regression in section 4.3.5
	11.9				HW4 due 11.9 11:59 PM Questions: Template:
	11.11	Ordinal Regression
	11.13	Poisson Regression
	11.16				Project Proposals due
4	11.18	Missing Data		Quiz 2 (GLMs)
	11.20	Missing Data
	11.23				HW5: GLMs
	11.25	Project work day (Last day of class)
	12.5				Project drafts due (optional)
	12.13				Final projects, Recorded presentations, and team feedback due 12 PM (Noon)