Check the Student Learning Center STAT 134 page for their study group times and drop-in times.
Instructor: David Aldous (aldous@stat)
Class time: MWF 9.00 - 10.00 in room 60 Evans
GSI Ying Xu (yingxu@stat)
This course does not have a lab session, but the GSI holds office hours (see below) and review sessions.
Textbook Probability, by Pitman.
If you are taking this course, I suggest you print out right now
General information about the course
Obviously these schedules might be changed as the semester progresses.
Some time you will want to print out
The text also contains practice exams.
(Aldous) Fridays 10.15 - 11.45 in 351 Evans
(Aldous) Mondays 2.30 - 3.30 at the Student Learning Center (atrium). Starting 9/10.
(Ying Xu) Mondays 4.00 - 5.30, Tuesdays 5.00-6.30, Wednesdays 1.10 - 2.10, in room 307 Evans.
If you email us put "STAT 134" in subject line.
Lecture 1 (M 8/27). Introduction.
Lecture 2 (W 8/30). Proportions, conditional probability examples.
Lecture 3 (F 8/31). Monty Hall, independence, examples with independence.
Lecture 4 (W 9/5). Birthday problem, illustration of calculus approximation.
Lecture 5 (F 9/7). Illustrations of Bayes formula.
Lecture 6 (M 9/10). Binomial distribution.
Lecture 7 (W 9/12). Normal approximation to Binomial -- the big picture.
Lecture 8 (F 9/14). Normal approximation to Binomial -- examples.
Lecture 9 (M 9/17). Confidence intervals.
Lecture 10 (W 9/19). Poisson Approximation; hypergeometric distribution.
Lecture 11 (F 9/21). A genetics example, illustrating Bayes updating.
Lecture 12 (M 9/24). Random variables.
Lecture 13 (W 9/26). Joint distributions for 2 random variables.
Friday September 28. MIDTERM 1 (covers Chapters 1 - 2).
Lectures 14 and 15 (M 10/1 and W 10/3). Expectation: conceptual and mathematical properties.
Lecture 16 (F 10/5). Variance and standard deviation. Binomial and Posson cases.
Lecture 17 (M 10/8). Variance for Geometric. Collector's problem. Inequalities. Standardization.
Lectures 18 and 19 (W 10/10 and F 10/12). Normal Approximation (the central limit theorem) for sums and averages.
Lecture 20 (M 10/15). Poisson distribution: more math facts, use in modeling, use for approximate calculations. Repeated games
Lecture 21 (W 10/17). Random point in spatial region.
Lecture 22 (F 10/19). Continuous RVs; distribution functions and density functions.
Lecture 23 (M 10/22). Uniform and normal densities. Poisson random scatter example.
Lecture 24 (W 10/24). Poisson arrival process; model and math properties.
Lecture 25 (F 10/26). Hazard rates; distribution of Y = H(X), change of variable formula.
Lecture 26 (M 10/29). Examples, Cauchy distribution, linear transformatins. Simulating X = F^{-1}(U).
Lecture 27 (W 10/31). Rejection sampling. Gambling on a favorable game (intro to portfolio theory).
Lecture 28 (F 11/2). Alternative formula for expectation; order statistics and beta distribution.
Lecture 29 (M 11/5). Joint densities.
Lecture 30 (W 11/7). Examples with joint densities.
Friday November 9. MIDTERM 2 (covers Chapters 1 - 4).
Lecture 31 (W 11/14). Densities for sums, quotients etc.
Lecture 32 (F 11/16). Independent Normal RVs.
Lecture 33 (M 11/19). Conditional distributions and expectations: discrete case
Lecture 34 (W 11/21). Conditional distributions and expectations: continuous case
Lecture 35 (M 11/26). Linear prediction (regression), correlation, covariance.
Lecture 36 (W 11/28). Examples and interpretion of correlation; sampling.
Lecture 37 (F 11/30). Bivariate Normal distribution.
Lecture 38 (M 12/3). Bivariate Normal distribution; review examples with independent Normals.
Lecture 39 (W 12/5). Example (auto insurance) to illustrate conditional and joint distributions.
Lecture 40 (F 12/7). Misc review examples.
Wednesday December 19, 8.00-11.00am, room 60 Evans Hall. FINAL EXAM (covers Chapters 1 - 6).