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Week Date Lesson Reading Video Slides Slides (pdf) Class Activity Assignment Project
Conceptual review questions
WEEK 1 Tue, Aug 24 Module 0.1: Course overview and introduction
MODULE 1: MULTIPLE LINEAR REGRESSION
Thur, Aug 26 Module 1.1: Motivating example
Module 1.2: Introduction to multiple linear regression
Module 1.3: Model fitting and interpretation of coefficients
In-class analysis 1: Beer consumption in Sao Paulo I
WEEK 2 Tue, Aug 31 Module 1.4: Hypothesis tests, confidence intervals, and predictions
Module 1.5: Checking main regression assumptions
In-class analysis 2: Beer consumption in Sao Paulo II
NEW ASSIGNMENT: Data analysis assignment I
Thur, Sept 2 Module 1.6: Outliers and influential points
Module 1.7: Mean squared error and cross validation
Module 1.8: Transformations
In-class analysis 3: Beer consumption in Sao Paulo III
WEEK 3 Tue, Sept 7 Module 1.9: Special predictors, F-tests, and multicollinearity
Module 1.10: Bringing the MLR pieces together I (illustration)
In-class: Q&A and discussion session
DELIVERABLES: Data analysis assignment I due!
NEW ASSIGNMENT: Data analysis assignment II
Thur, Sept 9 Module 1.11: Model building and selection
Module 1.12: Bringing the MLR pieces together II (illustration)
In-class analysis 4: Beer consumption in Sao Paulo IV
Fri, Sept 10 Final project outline
MODULE 2: LOGISTIC REGRESSION
WEEK 4 Tue, Sept 14 Module 2.1: Odds, odds ratios, and relative risks
Module 2.2: Logistic regression with one predictor
Module 2.3: Logistic regression with one predictor (illustration)
In-class: Q&A and discussion session
DELIVERABLES: Data analysis assignment II due!
NEW ASSIGNMENT: Data analysis assignment III
Thur, Sept 16 Module 2.4: Model assessment and validation - binned residuals and roc curves
Module 2.5: Logistic regression with multiple predictors I
In-class: Q&A and discussion session
WEEK 5 Tue, Sept 21 Module 2.6: Logistic regression with multiple predictors II
Module 2.7: Aggregated outcomes; Probit regression
In-class analysis 5: Predicting nba wins I
Wed, Sept 22 | DELIVERABLES: Data analysis assignment III due!
NEW ASSIGNMENT: Team project I
MODULE 3: OTHER GENERALIZED LINEAR MODELS
Thur, Sept 23 Module 3.1: Poisson regression
Module 3.2: Poisson regression (illustration)
In-class analysis 6: Predicting nba wins II
WEEK 6 Tue, Sept 28 Module 3.3: Multinomial logistic regression
Module 3.4: Multinomial logistic regression (illustration)
In-class: Q&A session and team meetings for project
Thur, Sept 30 Team project I presentations
WEEK 7
Tue, Oct 5 No class: fall break
Thur, Oct 7 Module 3.5: Proportional odds model
Module 3.6: Proportional odds model (illustration)
In-class: Q&A and discussion session
DELIVERABLES: Team project I reports due
DELIVERABLES: Team project I evaluations due
Fri, Oct 8 Instructions for final project proposals
MODULE 4: MULTILEVEL/HIERARCHICAL MODELS
WEEK 8 Tue, Oct 12 Module 4.1: Introduction to multilevel/hierarchical models
Module 4.2: Multilevel/hierarchical linear models
In-class analysis 7: Do more beautiful professors get higher evaluations I
NEW ASSIGNMENT: Team project II
Thur, Oct 14 Module 4.3: Multilevel/hierarchical linear models (illustration I)
Module 4.4: Multilevel/hierarchical linear models (illustration II)
In-class analysis 8: Do more beautiful professors get higher evaluations II
WEEK 9 Tue, Oct 19 Module 4.5: Multilevel/hierarchical logistic models
Module 4.6: Multilevel/hierarchical logistic models (illustration)
In-class analysis 9: Do more beautiful professors get higher evaluations III
Thur, Oct 21 Team project II presentations
WEEK 10 Mon, Oct 25 | DELIVERABLES: Team project II reports and evaluations due!
MODULE 5: HANDLING MISSING DATA
Tue, Oct 26 Module 5.1: Introduction to missing data
Module 5.2: Imputation methods I
In-class: Q&A session and team meetings for project
NEW ASSIGNMENT: Data analysis assignment IV
Wed, Oct 27 | DELIVERABLES: Final project proposal due!
Thur, Oct 28 Module 5.3: Imputation methods II
Module 5.4: Multiple imputation in R
In-class: Discussion session
MODULE 6: INTRODUCTION TO CAUSAL INFERENCE
WEEK 11 Tue, Nov 2 Module 6.1: The potential outcomes framework and causal estimands
Module 6.2: Assignment mechanisms and the role of randomization
In-class: Q&A and discussion session
Wed, Nov 3 | DELIVERABLES: Data analysis assignment IV due!
NEW ASSIGNMENT: Data analysis assignment V
Thur, Nov 4 Module 6.3: Unconfoundedness and overlap
Module 6.4: Regression-based estimation and covariate balance
Module 6.5: Stratification and matching
In-class analysis 10: Right Heart Catheterization I
WEEK 12 Tue, Nov 9 Module 6.6: Propensity scores
Module 6.7: Causal inference using propensity scores
Module 6.8: The minimum wage analysis
In-class analysis 11: Right Heart Catheterization II
Wed, Nov 10 | DELIVERABLES: Data analysis assignment V due!
MODULE 7: INTRODUCTION TO TIME SERIES MODELS
Thur, Nov 11 Module 7.1: Introduction to time series analysis
Module 7.2: Stationarity and autocorrelation
In-class: Q&A and discussion session
WEEK 13 Tue, Nov 16 Module 7.3: AR and MA models
Module 7.4: Time series analysis (illustration)
In-class: Q&A and discussion session
MODULE 8: OTHER TOPICS
Thur, Nov 18 Module 8.1: Random number generation
Module 8.2: Bootstrap
Module 8.3: Monte Carlo
In-class: Polynomial regression; local regression
WEEK 14 Mon, Nov 22 | DELIVERABLES: Upload final project presentations
Tue, Nov 23 Module 8.4: Classification and regression trees
Module 8.5: Ensemble tree methods
In-class: wrap-up and open OH for final projects
Thur, Nov 25 Reading week: work on final projects
WEEK 15 Tue, Nov 30 Reading week: work on final projects
Thur, Dec 2 Reading week: work on final projects
WEEK 16 Tue, Dec 7 Reading week: work on final projects
WEEK 17 Sun, Dec 12 | DELIVERABLES: Final project reports due