**Meeting Time:**MW 9:30-11, F 10-11 Room 32-082.**Final Exam:**Friday May 25 9:00-12:00, Room 32-082**Office hours:**Instructor: David Vogan, 2-355, dav@math.mit.edu, phone 617-253-4991

TA: Nicholas Triantafillou, 2-239A, ngtriant@mit.edu

TA: Guangyi Yue, 2-333A, gyyue@mit.edu

TA: Richard Zhang, 2-490, zrichard@mit.edu

**End of term office hours:**Regular office hours will not meet May 11-20.

Richard Zhang, 2-490, office hours: Wednesday, May 23 10 am - 12 noon.

Nicholas Triantafillou, 2-239, Wednesday, May 23 5 - 7 pm.

Guangyi Yue, 2-333A, office hours: Thursday, May 24 10 am - 12 noon.

David Vogan, 2-355, office hours: Thursday, May 24 1 - 5 pm.

**Course materials:**Here is a calendar/syllabus and due-date schedule.

Here is general information including a grading scheme.

Here are policies about collaboration; Piazza is a great way to collaborate.

Here is R in five easy pieces.

**Readings**(Each link should work after the preceding class.)- Introduction to the class.
- Class 1 preparation material (Counting) (Yes, you should read it even after Class 1.)
- Class 2 preparation material (Probability) (But now start reading BEFORE class!)
- Class 3 preparation material (Conditional probability)
- Class 4 preparation material (Discrete random variables)
- Class 5 preparation material (Variance, continuous random variables)
- Class 5.5 preparation material (Gallery of continuous random variables)
- Class 6 preparation material (Law of large numbers, CLT)
- Class 6.5 preparation material (math for CLT)
- Class 7 preparation material (Joint distributions)
- Class 7 preparation material (Covariance and correlation)
- Class 10 preparation material (MLE)
- Class 11 preparation material
- Class 12 preparation material
- Class 13 preparation material
- Class 14 preparation material (beta distributions)
- Class 14 preparation material (continuous priors, continuous data)
- Class 15 preparation material
- Class 16 preparation material (choosing priors)
- Class 16 preparation material (probability intervals)
- Class 17 preparation material (Bayesian versus frequentist statistics)
- Class 17 preparation material (significance testing)
- Class 18 preparation material
- Class 19 preparation material
- Class 20 preparation material
- Class 21 preparation material (confidence intervals)
- Class 23 preparation material (confidence intervals continued)
- Class 24 preparation material (Bootstrapping)
- Class 25 will continue with bootstrapping, then practice in R. NO code to hand in!
- Class 26 preparation material (Linear regression)
- Class 27 review problems.
- Class 27 review problem solutions.

**Class slides**To be posted shortly before each class. After class they will be replaced by a version including solutions to many of the board problems in the slides.- Class 1 slides
- Class 2 slides
- Class 3 slides
- Class 4 slides
- Class 5 slides
- Class 6 slides
- Class 7 slides
- Class 8 slides
- Class 9 review slides
- Class 10 slides
- Class 11 slides
- Class 12 slides
- Class 13 slides
- Class 14 slides
- Class 15 slides
- Class 16 slides
- Class 17 slides
- Class 18 slides
- Class 19 slides
- Class 20 slides
- Class 21 slides
- Studio 10 review slides
- Solutions to Studio 10 review problems
- Class 23 slides
- Class 24 slides
- Class 25 slides
- Class 26 slides
- Class 27 slides

- upload site for completed studio code. The upload site time-stamps your file. If you want to amend your work before the due time (usually midnight on the day of the studio) then you can upload a second version. I will discard all but the last one submitted.
- If the upload site does not work for you (for example, if you don't have MIT certificates) then you can email completed code to dav@math.mit.edu.
**Studios**(These links won't contain reliable versions of what you're to do until a day or two before the studio.)- studio1.zip It's reasonable to make a directory called "R" and to unzip this file (then later studio2.zip...) in the directory "R". You should certainly keep in this directory a copy of CLEARALL.R (to source before finally testing code for submission) and probably also commandsFromClass.r.
- studio2.zip
- studio3.zip 2018
- studio5.zip 2018
- studio6.zip 2018
- studio7.zip 2018
- studio8.zip 2018
- studio9.zip 2018
- studio11.zip 2018
- studio12.zip R problems mostly about bootstrapping and confidence intervals. We'll look at some in Class 25 on May 10; there is
**nothing to hand in!** - RQuiz zip file
- RQuiz-solutions.r

**Studio solutions**(These links won't work until a day or two after the studio.)- studio1-solutions.r
- studio2-solutions.r
- studio3-solutions.r
- studio5-solutions.r
- studio6-solutions.r
- studio7-solutions.r
- studio8-solutions.r
- studio9-solutions.r
- studio11-solutions.r
- studio12-sol.r

**Problem sets**(The links won't work until about a week before the due date. If they don't work then, let us know.)- Problem Set 1 (due Monday 2/12 9:30 a.m.) Solutions
- Problem Set 2 (due Tuesday 2/20 9:30 a.m.) Solutions. One problem refers to "probability mass function" and "expected value." These are explained in the Class 4 preparation material, posted above.
- Problem Set 3 2018 (due Monday 2/26 9:30 a.m.) Solutions
- Problem Set 4 2018 (due Monday 3/5 9:30 a.m.)Solutions
- Problem Set 5 2018 (due Monday 3/19 9:30 a.m.) Solutions
- Problem Set 6 2018 (due Monday 4/9 9:30 a.m.) Solutions, R code.
- Problem Set 7 2018 (due Wednesday 4/18 9:30 a.m.) Solutions, R code.
- Problem Set 8 2018 (due Monday 4/23 9:30 a.m.) Solutions, R code.
- Problem Set 9 2018 (due Monday 5/7 9:30 a.m.) Solutions, R code.

**Exam prep material**- Practice Exam 1. Solutions
- Exam 1 review problems. Solutions
- Practice Exam 2. Solutions
- Practice materials for R quiz. Solutions
- Practice exam for material after Exam 2. Solutions
- Practice Final. Solutions. There was a typo in the beta distribution concept question (mixing up heads and tails). It's now fixed (May 22).