| Date | Weekly topic |
Homework |
R code |
SAS Code |
Reading
|
| Week Jan 9 |
Syllabus
View Schedule Lecture 1: Test for Binomial Proportions 1. Binomial Proportions 2. Bayesian analysis of two proportions |
Homework 1 |
Homework
template BinomPost.R |
RB Ch7 RB Ch10 |
|
| Week Jan 16 |
Lecture 2: RR and OR 1. Relative risk, odds ratio Lecture 3: Delta method for confidence interval Lecture4: Contingency Table: Fisher exact test Lecture5: Chi-squared test |
Homework 1 Due (1/28) Homework 2 task1.csv |
|
RB Ch13 |
|
| Week Jan 23 |
Lecture 6: Case-Control Method Lecture 7: Introduction to Logistic Regression 1. Bernoulli distribution 2. Logistic model 3. Interpreataion of logistic coefficients 4. Connection to 2x2 table 5. Diagnostics |
Logistic.R Logistic2.R |
HL Ch1 |
||
| Week Jan 30 |
Lecture 8: Statistical Inference of Logistic Regression 1. Likelihood function 2. Maximum likelihood estimation by IRLS |
Homework 2 Due (2/18) Homework 3 |
logisticIRLS.R MyNewton2.R |
HL Ch2, Ch3 |
|
| Week Feb 6 |
Lecture 9: Classification using Logistic Regression 1. ROC curves 2. Cross-validated errors 3. Bootstrapping for error assessment |
ROC.R |
HL Ch5 |
||
| Week Feb 13 |
Lecture 10: Machine Learning Algorithms for Classification 1. Regression Models 2. K-Nearest Neighbors 3. Naive Bayes 4. Discriminant Analysis |
Homework 3 Due (3/3) Homework 4 |
case-study-2-or-7.R ml-smoothing.R |
HL Ch7 AG Ch10 |
|
| Week Feb 20 |
Lecture 11: Machine Learning Algorithm for Classification II 1. Tree-Based Approaches 2. Ensemble Lecture 12: Conditional Logistic Regression 1. Model 2. Conditional Likelihood 3. Application to mathced case-control studies Handwriting1, HandwritingC1 HandwritingC2 Handwriting3. Lecture13: Multinomial Logistic Regression Handwriting1 |
trees.R Clogit.R Multinomial.R Alligator.R |
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| Week Feb 27 |
Lecture 14: Log-Linear Regression for Count Data 1. Poisson model 2. Log-Linear Regression 3. Interpretation of Coefficients |
CountData.R AcheHunting.R |
AG Ch8 |
||
| Week March 6 |
Spring Break-No Class |
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Week March 13 |
Midterm Exam (3/22) in Class (Cover Lecture 1 to Lecture 12) Lecture 15: Log-Linear Regression for Count Data (Cont.) Handwriting1, Handwriting2, Handwriting3, Handwriting4, Handwriting5 MidTerm: 3/16 at 2:20PM in Coliseum 3003 |
Homework 4 Due (4/3) Homework5 |
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| Week March 20 |
Lecture 16: Fixed vs. Ramdom effects models |
Homework 5 Due (4/14) |
KNN Ch25 |
||
| Week March 27 |
Lecture 17: Non-Linear Regression Models |
KNN Ch27 HLCh7 AG Ch12 |
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| Week April 3 |
Lecture 18: Models for Correlated Data |
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| Week April 10 |
Lecture 19: Models for Correlated Data II |
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| Week April 17 |
Lecture 20: GAM |
LocalLikelihood BikeShare |
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| Week April 24 |
Final Project Due Friday April 28 @ 5P |
Final Take Home Exam Hint |
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