GSIA, 45-734 Carnegie Mellon University Probability and Statistics II Spring, 2001-2002 (mini 4)

Syllabus

Lectures  section M: TR 6pm-7:50, GSIA 240

section F: TR 8pm-9:50, GSIA 152

Instructor  Bill Vogt

HBH 2116D, 268-1843 wilibear@andrew.cmu.edu

Office Hours T 5:00-6:00

R 5:00-6:00

Grader  Amitabh Sinha, asinha@andrew.cmu.edu
Textbook  Newbold, Paul (1995)

Statistics for Business & Economics, 4th ed Upper Saddle River, NJ: Prentice-Hall

Website  http://www.andrew.cmu.edu/course/45-734/index.htm
Software  Eviews by Quantitative Micro Software

1 Course Objectives

Our objective will be to build understanding of the linear regression model and other advanced statistical tools.

2 Prerequisites

This is the second course in a two course series in probability and statistics. The student is expected to be competent in algebra at the undergraduate level and to have successfully completed the first course, 45-733, or its equivalent.

3 Grading

The grade will be determined by performance on homework assignments, a midterm, and a final exam. Each will have equal weight. You are responsible for the content of the lectures, including any handouts, and chapters 12-15 in the text. The lectures cover material very similar to that presented in chapters 12-15 of the text.

4 Software

We will use a general purpose statistical software package called Eviews, made by Quantitative Micro Software. The software is required. It will be used to generate class examples, to do homework, and to take the tests. It should already be installed on the laptops of MBA students. It is available online and at the campus computer store.

5 Various Class Policies

1. Policy on Cooperation

• You may cooperate as much as you like on homework assignments; however, each individual must submit a separate assignment, and each individual will be separately graded on that assignment.

• You may not cooperate while taking exams.

2. Policy on Aids During Exams

• All exams are open book. You may use text, notes, calculators, computers, reference materials, etc.

• You may not communicate or cooperate with anyone on the exam.

• Please do not use the open book policy as a substitute for studying. If you need to look in your book, notes, etc for instructions on HOW TO DO the exam, you will surely run out of time.

3. Policy on Format of Assigned Work

• All work must be legible. Illegible is the equivalent of completely wrong.

• Spelling, grammar, style, etc do not “count” per se. However, anything which I (or the grader) cannot understand is wrong. Poor spelling, grammar, style, etc are often confusing.

4. Policy on Lateness

• Due dates for assignments are in the syllabus in the schedule section. Unless the instructor announces otherwise, these dates are binding.

• Late assignments are not accepted without either prior arrangement or compelling and verifiable reason.

• Assignments and homeworks will be returned to you in class. You are responsible for keeping them to compare against your final grade. I will use whatever grade I have recorded for your work (even if you think there is an error) unless you can show me the graded work so that I can see the error.

• You are responsible for collecting your graded work. Claims by students similar to “I turned in homework #2, but I don’t have a copy of it because you did not return it to me!” must be made within one week of the return date of the assignment. If you fail to make the complaint in time, I will use whatever grade I have in my records.

Syllabus 45-734 Spring 2001/2 5

6 Schedule

The class schedule below is tentative and likely will not be followed exactly.

Date Material, Book Sections Work
T Mar 12 Introduction, Correlation, Regression (ch 12)
R Mar 14 Ordinary Least Squares (ch 12)
T Mar 19 Gauss-Markov Theorem, Estimation
R Mar 21 Multivariate Regression (ch 13)  HWK 1 due
T Mar 26 Multivariate Regression (ch 13)
R Mar 28 Multivariate Regression (ch 13)  HWK 2 due
T Apr 2 Spring Break, no class
R Apr 4 Spring Break, no class
T Apr 9 Dummy variables (ch 14)  HWK 3 due
R Apr 11 Midterm – In class
T Apr 16 Specification (ch 14)
R Apr 18 Heteroskedasticity, Autocorrelation (ch 14) HWK 4 due
T Apr 23 Heteroskedasticity, Autocorrelation (ch 14)
R Apr 25 Analysis of Variance (ch 15)  HWK 5 due
T Apr 30 Analysis of Variance (ch 15)
R May 2 Final Exam – In class