
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)
9980480 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 seventyfive 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, Stemandleaf 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.
 Twosample 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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