All assignments are due at 11:59 PM Duke time unless otherwise noted. This schedule may be updated as the semester progresses with all changes documented here (E-mail announcements will be made prior to any potential changes).


Week Date Activity Link Due
01 Tue, Jan 17 Lecture The OLS Estimator (1)
02 Tue, Jan 24 Lecture The OLS Estimator (2) Homework 1 (sols)
03 Tue, Jan 31 Lecture The OLS Estimator (3) Homework 2 (sols)
04 Tue, Feb 07 Lecture Probability Distributions Homework 3 (sols)
05 Tue, Feb 14 Lecture The OLS Estimator (4) Homework 4 (sols)
06 Tue, Feb 21 Lecture Properties of Estimators (1) Homework 5 (sols)
07 Tue, Feb 28 Lecture Properties of Estimators (2) Homework 6 (sols)
08 Tue, Mar 07 Lecture The Gauss-Markov Theorem Homework 7 (sols)
09 Spring Recess
10 Tue, Mar 21 Lecture Maximum Likelihood Estimation
11 Tue, Mar 28 Lecture The Exponential Family Homework 8 (sols)
12 Tue, Apr 04 Lecture Generalized Linear Models (1) Homework 9 (sols)
13 Tue, Apr 11 Lecture Generalized Linear Models (2) Homework 10 (sols)
14 Tue, Apr 18 Lecture Generalized Linear Models (3) Homework 11 (sols)
15 Tue, Apr 25 Lecture Semester Review Homework 12 (sols)
16 Wed, May 03 Final, 2 - 5 PM Final (sols)


Header: The image in the header of this site is from a first edition printing of Carl Friedrich Gauss' work Theoria combinationis observationum erroribus minimis obnoxiae. In particular, it displays one of the concluding sections of the first half of the work, in which Gauss establishes the conditions under which the ordinary least squares estimator achieves the minimum variance among all possible linear unbiased estimators. This is the Gauss-Markov theorem, which, coincidentally, we will derive and study as the conclusion to the first half of our semester.