Instructor: Philip B. Stark. Office Hours

Course Description

Statistics 151 is an advanced undergraduate course in statistical models, emphasizing linear models.

The text is *Statistical Models: Theory and Practice* by David A. Freedman,
Cambridge University Press (2005).

The course makes heavy use of linear algebra.
You might want to supplement your reading with *Matrix Computations* by
G.H. Golub and C.F. Van Loan, Johns Hopkins University Press (1983) or another book
on linear algebra. See p.40 of *Statistical Models: Theory and Practice*
for more recommendations.

The course also expects you to be or become reasonably fluent in R, a mathematical/statistical programming language and environment. There will be homework assignments roughly every week. There are nine lab assignments during the term. Course grades will be based on homework, lab assignments, a group term project, and a final exam. I have not yet decided what the relative weights will be. Grades will not be "curved." I will not offer an alternative time for the final. If you cannot attend the final, take a different course.

Show your work on all assignments. Homework must be legible, preferably typewritten. If the reader cannot read your homework easily, it will be returned to you ungraded. Labs must be typed, preferably typeset using TeX or LaTeX (this is a good opportunity to learn how to write a scientific paper). Homework and labs will not be accepted after the due date.

I encourage students to work together on homework, labs, and--of course--group projects. However, you must write up your own work. On the other hand, final exams are strictly individual work, and collaboration and other forms of cheating will not be tolerated.

Topics

Experiments and observational studies, regression, residuals versus error terms,
matrix algebra, standard errors, generalized least squares,
normal theory of regression, the *F*-test, path models,
inferring causation from regression,
response schedules, types of veriables, maximum likelihood, probit and logit models,
latent variables, the bootstrap for estimating bias and
variance.

Labs are from the textbook by Freedman. Data for the labs is online here.

Rough Course Schedule

The schedule for the term project is in a separate document.

The following is rough and subject to change. Be sure to read ahead in the book! You will get much more from the lectures if you have at least skimmed the material first. Homework problems are labeled <chapter>.<exercise set>.<problem number>. Homework is due in section the week after it is assigned.

- Week 1 (8/27)
- Topic: Experiments and observational studies
- Lecture: start chapter 1.
- Reading: chapter 1.
- Homework: 1.A.1, 1.A.4, 1.A.5, 1.A.6, 1.A.7
- Lab: 1, due in section, week 3.
- Week 2 (9/3)
- Topic: Linear regression
- Lecture: finish chapter 1; start chapter 2
- Reading: chapter 2.
- Homework: 2.A.1, 2.A.3, 2.A.4, 2.B.1, 2.B.5, 2.B.8
- Week 3 (9/10)
- Topic: Linear regression
- Lecture: finish chapter 2
- Reading: chapter 3.
- Homework: 3.A.2, 3.A.5, 3.B.5, 3.B.14
- Lab: 2, due in section, week 4
- Week 4 (9/17)
- Topic: Matrix Algebra
- Lecture: start chapter 3
- Reading: chapter 4.
- Homework: 3.C.5, 3.C.7, 3.C.10, 3.D.1, 3.D.2, 3.D.5, 3.D.7
- Lab: 3, due in section, week 5
- Week 5 (9/24)
- Topic: Matrix Algebra; Multiple Regression
- Lecture: finish chapter 3; start chapter 4
- Reading: chapter 4.
- Homework: 3.E.2, 3.E.3, 3.E.7, 3.E.10, 4.A.1, 4.A.2, 4.A.4, 4.A.5
- Lab: 6, due in section, week 6
- Week 6 (10/1)
- Topic: Multiple Regression
- Lecture: continue chapter 4
- Reading: chapter 4.
- Homework: 4.B.1, 4.B.3, 4.B.9, 4.B.13
- Lab: 7, due in section, week 8
- Week 7 (10/8)
- Topic: Multiple Regression; Hypothesis Tests
- Lecture: finish chapter 4
- Reading: chapter 5.
- Homework: 4.C.2, 4.C.3, 4.C.4, 4.D.2, 4.D.3
- Week 8 (10/15)
- Topic: Multiple Regression; Hypothesis Tests
- Lecture: Start chapter 5
- Reading: chapter 5.
- Homework: 4.E.1, 4.E.3, 4.E.6, 4.F.1, 4.10.7
- Lab: 8, due in section, week 9
- Week 9 (10/22)
- Topic: Path Models
- Lecture: continue chapter 5
- Reading: chapter 5.
- Homework: 4.10.14, 4.10.20, 5.A.2, 5.A.3, 5.A.7, 5.A.8
- Lab: 4, due in section, week 11
- Week 10 (10/29)
- Topic: Path Models
- Lecture: finish chapter 5
- Reading: chapter 6.
- Homework: 5.B.1, 5.B.3, 5.C.2, 5.C.3, 5.C.5, 5.D.1, 5.D.2
- Week 11 (11/5)
- Topic: Maximum likelihood
- Lecture: start chapter 6
- Reading: chapter 6.
- Homework: 5.E.1, 5.E.2, 5.E.4, 5.E.5, 5.7.1, 5.7.3
- Lab: 10, due in section, week 14
- Week 12 (11/12)
- Topic: Maximum Likelihood
- Lecture: continue chapter 6
- Reading: chapter 6.
- Homework: 6.A.2, 6.A.4, 6.A.5, 6.A.8, 6.B.1, 6.B.2
- Week 13 (11/19) Thanksgiving
- Topic: Maximum Likelihood
- Lecture: finish chapter 6
- Reading: chapter 8.
- Homework: None
- Week 14 (11/26)
- Topic: Simultaneous Equation Models
- Lecture: start chapter 8
- Reading: chapter 8.
- Homework: 6.C.6, 6.D.7, 6.E.3, 6.E.6
- Lab: 11, due in section, week 15
- Week 15 (12/3)
- Topic: Simultaneous Equation Models
- Lecture: finish chapter 8
- Reading: review.
- Homework: None
- Final Exam: 12/18 12:30-3:30pm