Three-Toed Sloth

Advanced Data Analysis from an Elementary Point of View

At the intersection of Enigmas of Chance and Corrupting the Young.

2024

Course homepage

2019

Course Announcement

Course homepage

2017

Course homepage

2016

Course Announcement

Course homepage

2015

Self-Evaluation and Lessons Learned

Course homepage

2013

Course announcement and homepage

Lectures

1. Regression: Predicting and Relating Quantitative Features
2. The Truth About Linear Regression
3. Model Evaluation, Error and Inference
4. Smoothing Methods in Regression
5. Simulation
6. The Bootstrap
7. Writing R Code
8. Heteroskedasticity, Weighted Least Squares, and Variance Estimation
9. Splines
10. Additive Models
11. Testing Regression Specifications
12. Logistic Regression
13. Generalized Linear and Generalized Additive Models
14. Multivariate Distributions
15. Density Estimation
16. Relative Distributions and Smooth Tests
17. Principal Components Analysis
18. Factor Analysis
19. Mixture Models
20. Graphical Models
21. Graphical Causal Models
22. Identifying Causal Effects from Observations
23. Estimating Causal Effects from Observations
24. Discovering Causal Structure from Observations
25. Time Series

Homework

1. What's That Got to do with the Price of Condos in California?
2. The Advantages of Backwardness
3. An Insufficiently Random Walk Down Wall Street
4. How the North American Mammalian Paleofauna Got a Crook in Its Regression Line
5. It's Not the Heat that Gets to You, It's the Sustained Heat with Pollution
6. How the Hyracotherium Got Its Mass
7. Red Brain, Blue Brain
8. How the Recent Mammals Got Their Size Distribution
9. Cancelled
10. Brought to You by the Letters D, A, and G
11. Growth and Debt

Exams:
1. Nice Demo City, But Will It Scale?
2. Choosing a Better History
3. Final Exam

Self-Evaluation and Lessons Learned

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2012

Course announcement and homepage.

Lectures

1. Regression: Predicting and Relating Quantitative Features
2. The Truth About Linear Regression
3. Model Evaluation: Error and Inference
4. Smoothing Methods in Regression
5. The Bootstrap
6. Heteroskedasticity, Weighted Least Squares, and Variance Estimation
7. Splines
8. Additive Models
9. Writing R Code
10. Testing Regression Specifications
11. Hypothesis Testing and Statistical Evidence
12. Logistic Regression
13. Generalized Linear Models and Generalized Additive Models
14. GLM and GAM Examples
15. Multivariate Distributions
16. Density Estimation
17. Simulation
18. Relative Distributions and Smooth Tests
19. Principal Components Analysis
20. Factor Analysis
21. Mixture Models
22. Graphical Models
23. Graphical Causal Models
24. Identifying Causal Effects from Observations
25. Estimating Causal Effects from Observations
26. Time Series I, without latent variables
27. Time Series II, with latent variables

Homework

1. What's That Got to Do with the Price of Condos in California?
2. Advantages of Backwardness
3. How the Hyracotherium Got Its Mass
4. It's Not the Heat that Gets to You, It's the Sustained Conjunction of Heat with Elevated Levels of Atmospheric Pollutants
5. How the North American Mammalian Paleofauna Got a Crook in Its Curve
6. What Makes the Union Strong?
7. Fun with Density Estimation
8. Red Brain, Blue Brain
9. How the Recent Mammals Got Their Size Distribution
10. Separated at Birth
11. Brought to You by the Letters D, A, and G

Exams

1. Diabetes
2. Is This Test Really Necessary?
3. Final Exam

Self-Assessment and Lessons Learned

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2011

Course announcement.

Course homepage.

Lectures

1. Regression: Predicting and Relating Quantitative Features
2. The Truth About Linear Regression
3. Evaluating Statistical Models
4. Using Nonparametric Smoothing in Regression
5. Moving Beyond Conditional Expectations: Weighted Least Squares, Heteroskedasticity, Variance Functions
6. Density Estimation
7. Simulation
8. The Bootstrap
9. Re-capitulation and Q&A, no notes
10. Testing Regression Models
11. Splines
12. Additive Models
13. More on hypothesis testing
14. Logistic Regression and Logistic-Additive Models
15. Generalized Linear Models and Generalized Additive Models
16. GLM Practicals
17. Principal Components Analysis
18. Factor Analysis
19. Mixture Models
20. Mixture Model Examples and Complements
21. Graphical Models
22. Graphical Causal Models
23. Estimating Causal Effects
24. Discovering Causal Structure
25. Conclusion: Statistical Data Analysis

Homeworks

1. What's That Got to Do with the Price of Condos in California?
2. The Advantages of Backwardness
3. Old Heteroskedastic
4. An Insufficiently Random Walk Down Wall Street
5. Bootstrapping Will Continue Until Morale Improves
6. Nice Demo City, But Will It Scale?
7. Diabetes among the Pima
8. Fair's Affairs
9. Patterns of Exchange
10. Estimating with DAGs

Exams

1. Midterm: Urban Scaling, Continued
2. Second exam: Mystery Multivariate Data

Other Handouts

Writing R Functions

Re-Writing Your Code

Self-Evaluation and Lessons Learned