Economics | Introduction to Statistical Theory in Economics and Business: Honors
S370 | 1633 | Kaganovich

	S370  Statistical Analysis for Business and Economics:  Honors

Spring 1999,   Section 1633

Class meets:	11:15am - 12:30pm  MW  in WY 005/WY 125 (first class
meeting in WY 005)
Professor:  	Michael Kaganovich	
Office:	254 Wylie Hall, phone 855-6967; messages at 855-1021;  e-mail:

Required Computer Program:  Microsoft Excel
This program is available through the Spreadsheets submenu in all the
IUTS clusters.  Statistical applications of this computer package will
be emphasized in this class. Most of assignments and exams will be
computer-based, and the knowledge of  EXCEL's  statistical
applications will be required.  Familiarity with the most basic
spreadsheet commands in EXCEL is assumed.

Prerequisites:  M118 (Finite Mathematics) or equivalent completed
prior to this class is an absolute requirement.  At least concurrent
enrollment in a calculus class (the M119 level or above) is also
Course Objectives
This class builds on your overall quantitative concepts and skills, as
well as on the knowledge of basic probability and statistics you
obtained in your Finite Math class.  This class should substantially
enhance this very important component of your college education.  It
will provide
- the understanding of key statistical concepts used in economics and
- the knowledge of basic statistical methods of data analysis which
are rigorously founded in the theory of probability, and the ability
to apply these methods with the help of statistical tools available on
- the ability to draw statistical inferences, i.e., to interpret the
results obtained from the application of     statistical methods.
The course will cover the following topics:

Descriptive Statistics.
Basic Rules of Probability: a brief review, knowledge presumed within
the scope of M118.
Random Variables, Expected Values (knowledge of the basics within the
scope of M118 is presumed).
Finance applications are emphasized. Advanced topic: Expected Utility
and decisions under uncertainty
Probability Distributions (the most essential are: Binomial and Normal
Sampling Distribution of sample means and proportions
Point and Interval Estimation
Hypothesis Testing
Simple Linear  Regression and Correlation
Multiple Linear Regression