NYU Stern School of Business

Undergraduate College

C22.0003.003: REGRESS/FORCASTING MODEL

Spring 2011

Instructor Details

Frydman, Halina

hfrydman@stern.nyu.edu

998-0453

Tuesdays: 4:45-5:45pm, Thursdays: 2 - 3 pm

KMC 8-55

 

Shreya Jain

sj857@stern.nyu.edu

Mondays 1-2pm KMC 2-80

Wednesdays 2-3pm KMC 2-90


TF Hours and Location:

Mondays 1-2pm KMC 2-80;

Wednesdays 2-3pm KMC 2-90

 

Course Meetings

TR, 3:30pm to 4:45pm

KMC 2-90

 

Course Description and Learning Goals

The objective of this course is to introduce students to the powerful statistical technique of simple and multiple linear regression. This technique is widely used in business and other fields for modeling relationship between variables of interest.

 

Course Pre-Requisites

V63.0121 - Calculus 1, 4 credits

 

 

 

 

 

 

 

 

 

 

Course Outline

Topics 

 Reading Assigments from MBS and Lecture Notes

1. Review of basic statistical inference

for a population mean:  Confidence interval and

Hypothesis testing



5.1-5.3, 6.1-6.5 and Lecture Notes

 

2. Simple Linear Regression

Chapter 10 and Lecture notes

3. Multiple Regression Models

 

11.1 - 11.4, 11.6, pages 656-660

11.7, 11.11, 11.12, and Lecture Notes

 

Required Course Materials

Texts

1. Statistics for Business and Economics, 2nd custom edition for NYU by McClave, Benson, and Sincich (MBS), Prentice Hall, 2008. Chapter 10 and 11 in MBS will be covered.

The text (MBS), Students Minitab Software and Student's Solutions Manual are sold as one package at the NYU Main Bookstore on 18 Washington Place.

2. Lecture Notes (will be distributed in class and posted on Blackboard).

 

Computer Software and Data Sets

Minitab 16 will be used as statistical software for the course. All data files from MBS are available on the CD that comes with the MBS text and also at

                                 http://www.stern.nyu.edu/~gsimon/statdata.  

The documents  "A Quick Introduction to Minitab" and "Guide to Minitab Regression" will be distributed in class.

The only meaningful difference between the Student Minitab version sold at the bookstore and Minitab 16 support by Stern is the memory restriction of the former. For essentially all of our work Student Minitab will have sufficient memory.

 

Assessment Components

Requirements and Grading

 

There will be two exams including a final exam  and weekly assignments.  The grading distribution and dates of the examinations will be as follows:

 

Item

Grading Distribution

Exam and other Dates

Midterm

35%

February 8

Homework

20%

weekly

Final Examination

45%

March 10

 

Grading

At NYU Stern we seek to teach challenging courses that allow students to demonstrate their mastery of the subject matter.  In general, students in undergraduate core courses can expect a grading distribution where: 

Note that while the School uses these ranges as a guide, the actual distribution for this course and your own grade will depend upon how well you actually perform in this course.

 

Re-Grading

The process of assigning grades is intended to be one of unbiased evaluation. Students are encouraged to respect the integrity and authority of the professor’s grading system and are discouraged from pursuing arbitrary challenges to it.

If you believe an inadvertent error has been made in the grading of an individual assignment or in assessing an overall course grade, a request to have the grade re-evaluated may be submitted. You must submit such requests in writing to me within 7 days of receiving the grade, including a brief written statement of why you believe that an error in grading has been made.

 

Professional Responsibilities For This Course

Attendance

 

Participation

In-class contribution is a significant part of your grade and an important part of our shared learning experience. Your active participation helps me to evaluate your overall performance.
You can excel in this area if you come to class on time and contribute to the course by:

 

Assignments

 

Classroom Norms

 

Stern Policies

General Behavior
The School expects that students will conduct themselves with respect and professionalism toward faculty, students, and others present in class and will follow the rules laid down by the instructor for classroom behavior.  Students who fail to do so may be asked to leave the classroom. 

 

Collaboration on Graded Assignments
Students may not work together on graded assignment unless the instructor gives express permission. 

 

Course Evaluations
Course evaluations are important to us and to students who come after you.  Please complete them thoughtfully.

 

Academic Integrity

Integrity is critical to the learning process and to all that we do here at NYU Stern. As members of our community, all students agree to abide by the NYU Stern Student Code of Conduct, which includes a commitment to:

The entire Stern Student Code of Conduct applies to all students enrolled in Stern courses and can be found here:

Undergraduate College: http://www.stern.nyu.edu/uc/codeofconduct
Graduate Programs: http://w4.stern.nyu.edu/studentactivities/involved.cfm?doc_id=102505

To help ensure the integrity of our learning community, prose assignments you submit to Blackboard will be submitted to Turnitin.  Turnitin will compare your submission to a database of prior submissions to Turnitin, current and archived Web pages, periodicals, journals, and publications.  Additionally, your document will become part of the Turnitin database.

 

Recording of Classes

Your class may be recorded for educational purposes

 

Students with Disabilities

If you have a qualified disability and will require academic accommodation of any kind during this course, you must notify me at the beginning of the course and provide a letter from the Moses Center for Students with Disabilities (CSD, 998-4980, www.nyu.edu/csd) verifying your registration and outlining the accommodations they recommend.  If you will need to take an exam at the CSD, you must submit a completed Exam Accommodations Form to them at least one week prior to the scheduled exam time to be guaranteed accommodation.

 

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