 Statistics, Operations Research and Actuarial Science Department   STERN SCHOOL OF BUSINESS  NEW YORK UNIVERSITY   NYU Stern Syllabi Homepage
 Course: C22.0103.04 - Statistics for Business Control - Regression and Forecasting Models Semester: Fall 2000 Class Hours: MWR 11:00 - 12:15 Class Room: Tisch UC61 Instructor: Prof. Samprit Chatterjee Office Hours: MR 1:30 - 4:00 pm; W 1:00 -3:00 pm Office: 850 MEC Tel: (212) 998-0480 Email: schatter@stern.nyu.edu TA: Madhu Jalan Email: mj332@stern.nyu.edu Objective

The purpose of the course is to train students to formulate analytical problems, to survey statistical techniques, and to introduce technical concepts from the core departments of Stem. The course stresses applications, the technical aspects underlying the methods will be presented intuitively.

The computer will be used as a tool for problem solving. No computer programming background is necessary and detailed instruction for the computer package MINITAB (Version 11) will be provided. This program is available to students in the computer labs.

The mathematical prerequisite for the course is the equivalent of A63.0017. This includes a review of elementary algebra and analytical geometry, and the applications of differential calculus to business problems.

The course will be taught in lecture and discussion mode. Student participation is strongly urged.

There will be 3 seventy-five minute lectures each week. There will be a TA for the course and she will hold regular office hours. The schedule will be announced in class.

Homework Assignments

Assignments will be given out each week and must be submitted the following week for review. All assignments must be completed. There will be a group project, details of which will be given in class.

Course Requirements

• Homework = 10%
• Quiz 1 = 25%
• Quiz 2 = 25%
• Project = 10%
• Final = 30%

Text: Business Statistics by Example (5th ed), by Terry Sincich, 1996.

List of Topics

• Nature of statistics; Variables, Populations, Samples; Data Collection, Types of Data; Summarizing Data: Frequency Distributions, Stem-and-leaf plots, Histograms, Boxplot. Reading: Chapters 1 and 2.
• Descriptive Statistics; Typical Values and Measures of variability Percentiles; Grouped data. Graphical Data Analysis, Displaying data. Normal approximation for data; Measurement error; Bias; Outliers. Reading: Chapter 3.
• Probabilities; Sample spaces and events. Conditional probabilities; Addition and Multiplication rule. Random Variables and Expectations. Reading: Chapter 4.
• Probability Distribution: Mean and Variance. Discrete Distributions: Binomial and Poisson, Reading: Chapter 5.
• Continuous Distributions: Uniform and Normal.; Reading: Chapter 6.
• Random Sampling and Sampling Distributions; Central Limit Theorem; Standard Error. Reading: Chapter 7.
• Confidence intervals and Hypothesis testing, Testing population mean, binomial proportion. The t distribution. Sample size selection Reading: Chapter 8: Sect. 1 -.4; Chapter 1 0, Section 1.
• Two-sample comparisons: means and proportions. Reading: Chapter 8: Sect. 5 - 8; Chapter 11, Section 4 - 6.
• Scatter diagrams; Linear association and causation. Simple linear relationship. Method of Least Squares- Normality assumptions. Reading: Chapter 12: Section 1 - 4.
• Interpreting model outputs and results. Reading:, Chapter 12: Section 5 10.
• Residual analysis; Regression pitfalls; Transformations and Model selection.
• Autocorrelation and Autoregression. Regression Models for Forecasting.

Recommended Reading: A Casebook for a First Course in Statistics and Data Analysis. Chatterjee, Handcock, and Simonoff. 1995.

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