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Math 3/103
Introduction to Probability and Statistics
Winter 2014–15
MWF 10:00 AM // Baxter Lecture Hall
Contact Information Top
Kim Border, 205 Baxter Hall, x 4218,
Office Hours:
Fridays, 1:30–3:00 pm
Lead TA:
Marius Lemm,
Office Hours:
5:30 PM, Sunday
156 Sloan
Course Secretary:
Kristy Aubry, 253 Sloan, x 4087,
Communication Top
Announcements Top
Course Description Top

Introduction to the fundamental ideas and techniques of probability theory and statistical inference.

Ma 1abc. In addition, some familiarity with a scientific computing language or program (e.g., Matlab, Mathematica, R, NumPy) is assumed.

Probability will be covered in the first half of the term (using Pitman) and statistics (using Larsen and Marx) in the second half (see below for information regarding textbooks). Main topics covered are:

Recitation Sections Top
List of Recitation Sections and TAs
Section Recitation TA Office Hours
1 9:00 am Thursday
159 Sloan
Jake Marcinek
153 Sloan
3 PM, Saturday
2 9:00 am Thursday
153 Sloan
Dmtri Gekthman
155 Sloan
2:30 PM, Sunday
3 10:00 am Thursday
115 BCK
Andrei Frimu
155 Sloan
3:30, PM Sunday
4 10:00 am Thursday
101 Kerckhoff
Nathan Lawless
155 Sloan
4:30 PM, Sunday
5 10:00 am Thursday
19 Baxter
Seunghee Ye
160 Sloan
8 PM, Sunday
6 11:00 am Thursday
151 Sloan
Karlming Chen
155 Sloan
Noon, Sunday
7 1:00 pm Thursday
27 Gates
Jim Tao
155 Sloan
11 AM, Saturday
8 1:00 pm Thursday
107 Downs
Marius Lemm*
156 Sloan
5:30 PM, Sunday
9 2:00 pm Thursday
27 Gates
Pooya Vahidi
155 Sloan
6 PM, Friday

*Head TA

Policies Top
Late Work
As rule, late work is not accepted. This is to protect the TAs, who are talented hardworking students, just as you are. At the discretion of the Head TA, late homework turned in the day it is due, but after the 4:00 pm deadline will be accepted with a 25% penalty. (If there are extenuating circumstances, you must notify the Head TA in advance and a note from the Dean will be needed.) As partial compensation, your lowest homework score will be discarded. (Since the homework assignments have different weights, the Kasatkin Algorithm will be employed to decide which assignment to drop. It is designed to give you the best score possible.)
Your course grade will be based on the weekly homework (40%), the midterm (25% or 35%), and the final (35% or 25%). The weights on the final and midterm will put the greater weight on the better exam. In computing the homework average, your lowest homework score will be dropped. This is in lieu of a late homework policy: no late homework will be accepted without both prior notification of your TA by midnight the night before the set is due, and a note from the Dean. As this course is for a letter grade, no one will be excused from the final.
Homework will be typically be due at 4:00 pm on Mondays in the appropriate homework box outside 253 Sloan. (If Monday is a holiday [which happens twice this term] homework will be due on Tuesday. Assignment 0 is a major exception.) Problems (and later solutions) will be posted on this course webpage. You are encouraged to start the homework well in advance of the due date in order not to risk missing the deadline. Homework is turned in to locked boxes, so it can safely be submitted as soon as it is completed.
Collaboration is allowed on the homework, but your write-up must be in your own words and may not be copied. Collaboration is not allowed on the exams. Please ask for clarification if anything is unclear.

*Information is subject to change*

Textbooks Top

The required textbooks for the course are:

There will be additional readings from time to time, either as handouts or articles available on line.

There are other books that you may find useful for this course or perhaps later in life. Here are some of my recommendations.

Modern statistical practice is computationally intensive, but this course is not especially so. But you will have to use computers to do some of the assignments. Many of the people on campus that I have talked to recommend the statistical programming language R (the open source alternative to AT&T's S). The new Mathematica 9 and 10 claim to be highly integrated with R, but I haven't tried it yet. Others I have talked to rave about NumPy, an extension of Python that provides much of the functionality of Matlab. Still others continue to use other packages because they have invested a lot of effort in learning to use them. (I myself often use Mathematica because I started using it in 1992, so my recommendation of R falls into the category of do as I say, not as I do.) My son recommends R and this video as an endorsement.

Here are a couple of highly recommended books on R that I mostly have not read. But I find the first two to be useful.

Handouts Top

Please report any errors or suspected errors or confusing bits to the professor. You can use the anonymous feedback form if you wish.

Notes for Lectures:

Data for the Final

Supplementary handouts:

Homework Top