STA 230: Probability:Spring 2017
| Prof: | Sayan Mukherjee |
sayan@stat.duke.edu | |
OH: Wednesday 2:15-3:15 | 112 Old Chem |
| TAs: |
| Yimeng Jia
| | OH: | Wed. 12-1, Fri 9-10, 221A Old Chem | |
| Liyu Gong | | OH: | Mon 11:30-12:30, Fri
11:30-12:30, 221A Old CHem | |
| Wenli Shi | | OH: | Tue
11:30-12:30, Th 11:30-12:30 221A Old Chem | |
| Class: | T/Th
1:25-2:40pm | | | | Old Chem 116 |
Description
This is a basic calculus-based first course in the theory and
application
of probability. It develops quantitative methods for solving problems
that
involve uncertainty, and provides a foundation for the further study
of
statistics or random processes. Many probability calculations are
based on
summing infinite series or on evaluating integrals so calculus at the level of MTH112*
is a prerequisite for this course. If you are unsure about your
calculus preparation, try this
diagnostic quiz.
The course text is Jim Pitman, Probability.
You will also have access to Elementary Probability for Applications
by Rick Durrett on Sakai.
In the syllabus below I have posted for each lecture what section in
Pitman or Durrett the material is covered in.
I have also posted in the syllabus below links to video lectures by
Jonathan Mattingly and Joe Blitzstein that cover the lecture material
as well.
For each lecture (typically one lecture will cover two classes). I
have posted where in both books the material is covered, links to
videos covering the material, a short note on the key topics/ideas,
in these lectures, and the homework.
The homework, quiz, and exam scores will be reported through
Sakai. Also before every class you will be expected to take a
short quiz. This is to make sure you have looked at the material
before class.
Inverted classroom
This course uses the inverted classroom approach. This means that
students will either watch video lectures and/or read book chapters before
class. Class will be used for exercises and examples that highlight
the core concepts in the material. To ensure that students go through
materials before class there is quiz to take online that needs to
be completed the day before each lecture.
The class exercises will be group exercises to help solidify concepts
in the lectures and give some examples of how these concepts are
used. The exercises will be done in small groups (3-4) and will
typically involve pencil-and-paper work. Sometimes you will also run
some code (in Rstudio) which will be provided in advance to help
illustrate ideas.
Grading
The course grade is based on two midterms (20%) each, quizes (15%),
homeworks (15%), final (30%). The exams are in class and closed books.
The quizes will be completed online via Sakai.
No make-up exams will be given and there will be no make-ups for
homeworks or quizes.
You cannot pass the class if you do not take the final.
Homework is graded out of 100. Late work will receive no
credit. Lowest homework score will be dropped. Even if you have an
excused absence you must turn in your homework.
There is a Piazza course
discussion page. Please direct questions about homeworks and other
matters to that page. Otherwise, you can email the instructors (TAs
and professor) at sta230-ta@duke.edu. Note that we are more likely to
respond to the Piazza questions than to the email, and your classmates
may respond too, so that is a good place to start.
- (Jan 12, 17) Outcomes and events:
- (Jan 19, 24) Conditional probability:
- (Jan 26, 31) Distributions I: Binomial, Poisson, Normal:
- (Feb 2, 7) Distributions II: Hypergeometric, Multinomial, Counting:
- (Feb 9, 14) Random variables: Expectations, Variances, Moments:
- (Feb 16, 21) Continuous random variables: Cummulative
distributions, Probability densities, Change of variables, Order statistics:
- (Feb 23) Review
- (Feb 28) Exam 1:
Histogram of results
- (March 2, 7, 9) Joint distributions: Marginals, Covariance, and Correlation
- (March 21, 23) Conditional distributions and expectations:
- (Mar 28, 30) Law of large numbers:
- (Apr 4))
Review: Problems 2-7
and Solutions and
Another exam
- (Apr 6) Exam 2
Histogram of results Mean: 42.5
- (April 11,13) Central limit theorem:
- (April 18, 20) Markov chains:
- (April 25) Review:
Final
and Solutions and
Another exam and Another exam
- (May 2: 7-10pm) Final exam
Histogram for Sta230 Histogram for Math230
