Stats 120B/Math 131B - Winter 2013

Stats 120B/Math 131B: Introduction to Probability and Statistics

Winter 2013

Department of Statistics

University of California, Irvine

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Course Syllabus and Grade Dispute Form
Instructor and Class Information
R References
Data Sets Used in Class and on Homework
Class Calendar: Material covered, handouts, and homework assignments by day
Homework Solutions

Course Syllabus

Download the course syllabus here.

Grade Dispute Form: You must print and fill out this cover sheet (pdf) and attach the homework/exam in dispute in order for your grade to be reconsidered. Please read the instructions on the cover sheet carefully.

DeGroot and Schervich webpage, with links to errors in the textbook.

Instructor and Class Information

Lecture: Tue/Thurs 3:30-4:50pm DBH 1500
Discussion: Tue 2:00-2:50pm SSTR 103

Instructor	Teaching Assistant
Stacey Hancock, PhD	Kevin Heins
Office: 2204 DBH	Office Hours Location: 2013 DBH
Phone: (949) 824-9795
Email: stacey.hancock_at_uci.edu	Email: kheins_at_uci.edu
*Office Hours:* Tue/Thurs 10:00-11:30am or by appointment.	Office Hours: Wed 10:00am-12:00pm; Thurs 1:00-2:00pm.

Please email the instructor or TA only for personal questions. Post all questions about course material, homework, exams, etc. on our EEE MessageBoard:
https://eee.uci.edu/boards/w13/stats120B

R References

Download and Install R:
The R Project for Statistical Computing
R for Windows FAQ
R for Mac FAQ

Quick R References:
Jones R Reference Page
Short R Reference Card

Online R Tutorials and Documentation:
R FAQ
R Wiki
An Introduction to R (pdf) (html)
Verzani Simple R Notes

R Books: The following two books are free to download on link.springer.com if you're accessing it on UCI campus:

A Beginner's Guide to R by Alain F. Zuur, Elena N. Ieno, and Erik Meesters; Springer, 2009.
Introductory Statistics with R by Peter Dalgaard; Springer, 2008.

Data Sets

Fusion Data (copy and paste into R)
Anorexia Data (copy and paste into R)

Class Calendar

This calendar will be updated after each class. Check back periodically for required reading, additional handouts and references, homework assignments, and lecture notes.

Date	Material Covered (Tentative schedule; may be updated after each class)	Date HW Due	Assignment
Week 1:
Discussion	No discussion this week.
Tue. Jan. 8	Lecture 1 Notes Introduction to Statistics 120B; Chapter 7, Sections 7.1-7.2: statistical inference, prior, posterior, and likelihood. Review of Statistics 120A material as needed (Ross; Chapters 1-6 in DeGroot and Schervish).
Thurs. Jan. 10	Lecture 2 Notes "Review" of joint distributions (Section 3.7), multivariate normal distribution (not in text), maximum likelihood estimation (Sections 7.5-7.6), bias and MSE (Sections 4.5 and 8.7). Reviewed covariance and correlation (Section 4.6), and expected values for functions of random vectors (end of Section 4.1).	Tue. Jan. 22	Homework #1
Week 2:
Discussion	Introduction to R.
Tue. Jan. 15	Lecture 3 Notes Finish Lecture 2 notes on maximum likelihood estimation, bias and MSE; method of moments (Section 7.6), return to Bayes estimation (Sections 7.3-7.4).
Thurs. Jan. 17	Start Lecture 3! (No new notes.)
Week 3:
Discussion	Review of Bayes formula and how it applies when deriving conditional distributions (e.g., posterior distributions): Sections 2.3, Theorems 3.6.3 and 3.6.4 on p. 148.
Tue. Jan. 22	Lecture 4 Notes Finish Lecture 3; leftovers from Bayes estimation. R Code for Gamma/Beta examples in Lecture 3.	Tue. Jan. 29	Homework #2
Thurs. Jan. 24	Finish Lecture 4 notes. (No new notes.) Note: After this lecture, we will have covered Chapter 7, Sections 7.1-7.6. In Section 7.2, skip the "Sequential Observations and Prediction" section (p. 391-393). In Section 7.6, skip the "Numerical Computation" (p. 428-30), "The EM Algorithm" (p. 434-39), and "Sampling Plans" (p. 439-41) sections. We are skipping Sections 7.7-7.9.
Week 4:
Discussion	Review for Midterm 1.
Tue. Jan. 29	Lecture 5 Slides Lecture 5 Notes Chapter 8, Section 8.1: introduction to the sampling distribution of a statistic.
Thurs. Jan. 31	MIDTERM 1 Midterm 1 Information, Material, and Practice Problems Solutions to Practice Problems - work through the problems before looking at the solutions! Midterm 1 grade distribution and common errors Midterm 1 Solutions
Week 5:
Discussion	Sampling distribution simulations with Reese's Pieces. R code used in discussion.
Tue. Feb. 5	Lecture 6 Notes Finish Lecture 5 notes; Chapter 8, Sections 8.2-8.4: Chi-square distributions, sampling distributions of the sample mean and sample variance for normal data; t-distributions. R Code for Exponential data example in Lecture 5 Notes.	Thurs. Feb. 14	Homework #3
Thurs. Feb. 7	Lecture 7 Notes Finish Lecture 6 notes; Chapter 8, Section 8.5: Confidence intervals.
Week 6:
Discussion	Review Chapter 8 concepts.
Tue. Feb. 12	Lecture 8 Notes Finish Lecture 7 notes; finish Section 8.5.
Thurs. Feb. 14	Lecture 9 Notes Finish Lecture 8 Notes; Chapter 8, Section 8.6. Normal QQ-plot matching handout. Normal QQ-plot solutions.	Tue. Feb. 26	Homework #4 If you turn this assignment in by Friday, Feb. 22 at 5pm, we will have it graded for you before Midterm 2.
Week 7:
Discussion	More practice with R; normal qq-plots. R code used in discussion.
Tue. Feb. 19	No new lecture notes! Finish Lecture 8 Notes and start Lecture 9 Notes. Magic eye example: Try to find the duck! (Not all the little ducks, but the big 3-D duck in the middle.) For an easier example, and instructions, click here. R Code for fusion data example (from Lecture 8). R Code for Bayesian credible interval example (from Lecture 9).
Thurs. Feb. 21	Lecture 10 Notes Finish Lecture 9 Notes. Supplemental example for Section 8.6; Section 8.7 on unbiased (and biased) estimators and MSE. R Code for Bayesian credible interval example with Exponential data. R code used in lecture. Note: After this lecture, we will have covered Chapter 8, Sections 8.1-8.7. In Section 8.3, skip the "Proof of Theorem 8.3.1" section (p. 476-478). In Section 8.4, skip the "Derivation of the p.d.f." section (p. 483-484). In Section 8.6, skip the "Improper Prior Distributions" section (p. 502-504) (though it is an interesting fact that improper priors often lead to frequentist confidence intervals). We are skipping Section 8.8.
Week 8:
Discussion	Review for Midterm 2.
Tue. Feb. 26	Lecture 11 Notes Finish Lecture 10 notes; Start Chapter 9.
Thurs. Feb. 28	MIDTERM 2 Midterm 2 Information, Material, and Practice Problems Solutions to Practice Problems - work through the problems before looking at the solutions! Instructions for Midterm 2 corrections, Midterm 2 grade distribution, and common mistakes and comments. Midterm 2 Solutions
Week 9:
Discussion	Randomization tests to demonstrate p-values.
Tue. Mar. 5	Lecture 12 Slides Lecture 12 Notes Chapter 9, Sections 9.1 and 9.5 on hypothesis testing and t-tests.	Tue. Mar. 12	Homework #5
Thurs. Mar. 7	Lecture 13 Notes More details and additional examples from Section 9.1; exact binomial tests and confidence intervals. R code for binomial test and confidence interval examples R session from lecture
Week 10:
Discussion	Review for final exam.
Tue. Mar. 12	Lecture 14 Notes Finish Section 9.5 on one-sample t-test and paired t-test; Section 9.6 on two-sample t-test; Section 9.7 on F-distributions and tests for equal population variances. R code for Anorexia example: two-sample t-test and test for equal variances R session from lecture	Tue. Mar. 19 by 6pm	Take-home final exam handed out at the end of lecture.
Thurs. Mar. 14	Lecture 15 Notes Finish binomial confidence interval from Lecture 13; Likelihood ratio tests (Section 9.1). R session from lecture (continuing R code from Mar. 7) R plot to find binomial confidence interval
Final Exam:	Due by 6pm, Tue. Mar. 19. Final Exam Solutions Have a wonderful spring break!

Homework Solutions

Homework solutions will be posted the evening homework is due (or early the next morning).

Homework Assignment	Solutions
Homework #1	Homework #1 Solutions
Homework #2	Homework #2 Solutions
Homework #3	Homework #3 Solutions
Homework #4	Homework #4 Solutions
Homework #5	Homework #5 Solutions