Date 
Lecture Notes 
Homework and Resources 
Probability & Distributions 
Aug 23 
Sample Spaces, Kolmogorov Axioms, Counting 
HW #1, due Aug 30 
Aug 25 
Conditional Probability, Independence, Bayes Rule, see also Ewens & Grant, p. 36, 3641 or Baron p. 

Aug 30 
Bayes, Discrete Random Variables, Expectation, see Ewens & Grant, p. 6,7, 1316; Baron p. 
HW #2, due Sep 6 
Sep 1 
Moment generating function, discrete distributions, see Ewens & Grant, p. 719 

Sep 6 
Joint discrete distributions, intro to continuous distributions, see Ewens & Grant, p. 5557, 24, 28, 42 
HW #3, due Sep 13 
Sep 8 
Continuous random variables and special distributions 

Sep 13 
Central Limit Theorem and Corollaries, Relationship: Poisson and Gamma, Multivariate random variables, convolution see Ewens & Grant, p. 5960, 7576, 133. 
HW #4, due Sep 20 
Basic Simulations 
Sep 15 
Intro to Random Number Generators,
R code


Sep 20 
Review of material using Simulations.,
R code

Review material for exam. previous exam. focus on questions 3 and 4. 
Statistical Inference 
Sep 22 
Midterm Exam (2:10  3:00). Intro to Statistics: graphical summaries and estimation


Sep 27 
Review of exam, Graphical summaries and estimation.


Sep 29 
Parameter Estimation, Intro to Confidence Intervals

Project I, due dates Nov 3/Nov 8 (midnight). 
Oct 4 
Confidence Intervals: large and small samples

HW #5, due Oct 11 
Oct 6 
Intro to R, R code, R reference card


Statistical Models 
Oct 11 
Hypothesis testing,
R code, data

HW #6, due Oct 18 
Oct 13 
Intro to Linear Models: Regression and Anova


Oct 18 
Normal Model, Residuals and Prediction in Linear Models, Model Comparisons,
R code


Oct 20 
General Linear Models in R,
R code


Oct 25 
Midterm Exam II, Logistic Regression

HW #7, due Nov 1, cheese data , mystery 
Oct 27 
Logistic Regression

R code 
Nov 1 
Trees and Random Forests in R, R code

no homework this week  focus on project submission 
Specialized Topics of interest 
Advanced Simulations 
Nov 3 
More Theoretical look on Trees and Random Forests, , R code


Nov 8 
Bootstrapping, Intro to Markov Chains

HW #8, due Nov 15, Final Project, due dates Nov 15, Nov 29, Dec 6 
Nov 10 
Markov Chains


Nov 15 
Hidden Markov Models,
R code


Nov 17 
Metropolis Hastings, Gibbs Sampling,
R code


Nov 22/24 
Thanksgiving Break


Nov 29 
Clustering Methods, R code


Dec 1 
Clustering, Project Discussion


Final Projects and Exam 