Honors | Statistical Analysis for Business and Economics: Honors
S370 | 1598 | 1633 Kaganovich, M


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:
econstat@indiana.edu

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 required.
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 business;
- 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     computer;
- 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
distributions)
Sampling Distribution of sample means and proportions
Point and Interval Estimation
Hypothesis Testing
Simple Linear  Regression and Correlation
Multiple Linear Regression