Joan Bruna, email@example.com, 419 Evans
office hours: Fri 1pm-3pm
201 and 203: Rebecca Barter, firstname.lastname@example.org, 446 Evans
office hours: Wed 9am-11am
202: Jonathan M Levy, email@example.com, 444-446 Evans
MWF 11am-noon, Evans 60
The course will introduce fundamental concepts in Mathematical Statistics and Statistical Inference,
and its applications to modern Data Analysis. We will pay special attention to the following aspects:
- Survey Sampling: how to quantify the amount of information and uncertainty extracted from data.
- Parametric Estimation: how to adjust parametric models to the data.
- Hypothesis Testing: how to quantify the uncertainty of our estimates.
- Linear Regression Models: how to model and explain (linear) dependencies in the data.
The theory will mostly follow John Rice's Mathematical Statistics and Data Analysis, 3rd edition, 2007. We will mostly cover Chapters 7 to 14.
Other recommended books and notes are:
- M.H. De Groot, Probability and Statistics, 3rd Edition, Addison-Wesley, 2001.
- Course Notes from Richard Weber: http://www.statslab.cam.ac.uk/~rrw1/stats/
Slides and Course Notes:
They will be available here.
either R or Python.
Calculus and Probability at the level of Stat 134
Basic computer skills (coding and data manipulation).
Grades will be based on a midterm, a final exam, homework, and labs.
The midterm will count for 20% of the grade; the score on the midterm will be replaced by the score on the final if the latter is higher. If you miss the midterm, the score on it will be your score on the final (no alternative dates for the midterm).
The final exam will count 50%. There will be no alternative dates, so if you can't take the exam at this time, don't take the course.
Homework will be assigned every week and will count 10%. Your two lowest homework grades will be dropped. Assignments will be posted in this page. Homework will be collected in class on Fridays.
There will be 3 lab assignments (note that these are distinct from the exercises covered in the lab sections); this component of the course will count 20%. They require data analysis using the statistical software R and perhaps Python.
Midterm: Friday, March 13th.
Final: Tuesday, May 12th, 7-10pm.
You are encouraged to work together with others on the homework, but you must write up your own solutions. The same applies to labs -- you must ultimately do your own coding. So, for example, if a lab assignment involved taking a random sample, your random sample had better not be identical to any other in the class. No collaboration is allowed on exams. Cheating will be taken seriously and the penalties will be severe.
The syllabus page shows a table-oriented view of the course schedule, and the basics of course grading. You can add any other comments, notes, or thoughts you have about the course structure, course policies or anything else.
To add some comments, click the "Edit" link at the top.