- Data Analysis and Modelling For Managers
Semester: Summer 2000
Class Hours: Monday 5:30-9:20PM, Wednesday 7:30-9:20PM
Instructor: Professor Aaron Tenenbein
Office: K-MEC 8-58 Tel: (212) 998-0474
A. COURSE DESCRIPTION
The purpose of this course is to provide a survey of quantitative techniques which are applicable to decision making in a business environment. The course stresses applications; the technical aspects underlying the applied methods used will be presented intuitively.
This course begins with a discussion of the Linear Programming technique which is used to determine how to allocate your resources to maximize your objectives. This technique has wide applications in production planning, asset allocation problems, and media decisions.
The second topic is Data Collection and Analysis. This topic discusses the methodology involved with extracting information for data which are collected and using these data to aid in the decision making process.
The third topic involves a discussion of probability and probability distributions. As many business decisions have to be made in the face of uncertainty, it is very important to understand the applied concepts of probability in order to aid the decision making process.
The next topic is Sampling and Statistical Inference. In most cases business decisions will have to be made by using information from sample surveys. The key question is how to determine the reliability of the sample information. The concepts of probability are used to answer these questions so that the information contained in the sample can be used to infer the information required.
The last topic is regression analysis and forecasting. This topic is probably one of the most important and widely used statistical techniques. It has a wide variety of applications to business problems in accounting, economics, finance, management, marketing, and operations.
Generally regression analysis is involved with the prediction or forecasting of a given variable (called the dependent variable) given knowledge of the values of other variables (known as the independent variables). In marketing, a media buyer would like to measure the impact of advertising and other variables on sales. A financial analyst may want to predict the return on a stock from the return on an index. A production engineer may want to predict the time it takes to complete a given task for given characteristics of that task.
Closely associated with regression analysis is correlation analysis which is concerned with the measurement of the relationship between different variables. This can be used to study such things as the correlation between advertising and sales of a product and between the rate of return of a stock fund and the rate of return of a bond fund. Obviously correlation and regression analysis are closely related.
This course will provide a survey of the use of regression and correlation analysis and the use of regression analysis in forecasting. The computer program MINITAB will be used extensively in order to carry out these analyses on cases which will be provided.
The computer will be used extensively as an aid for performing statistical and operations research analyses on cases which are provided. No computer programming background is necessary and instructions for the use of computer packages on the IBM PC will be provided.
The mathematical prerequisite for this course is the equivalent of B00.2002. This includes a review of elementary algebra and analytic geometry, mathematics of finance, and the applications of differential calculus to business problems.
The course will be taught in a lecture and discussion
mode. Student participation is strongly urged.
B. REQUIRED READING
C. PROBLEM ASSIGNMENTS AND TESTS
Problem assignments will be given at the end of each session. Many of these problems assignments involve the use of the computer. Two mid-term examinations and a final examination will be scheduled. The basis for the grade is as follows:
|Assignments and Group Participation||
D. COURSE TOPICS AND READING ASSIGNMENTS
|1.||Linear Programming||EGS: Chapters
C: Pages 6-16
|2.||Data Collection and Analysis||H: Chapters
H: Chapter 11: Sections: 1,2,5
and Probability Distributions
(Including the Binomial and Normal Distribution)
|H: Chapter 3:
H: Chapter 4: Sections: 1,2,3
H: Chapter 5: Sections: 1,2,4
|4.||Sampling and Statistical Inference||H: Chapter 6:
H: Chapter 7: Sections: ALL
H: Chapter 8: Sections 1,3,4,5
|5.||Correlation Analysis, the Simple Regression Model and Multiple Linear Regression Models||H: Chapters 11-13
C: Pages 33-55