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: 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