QUANTITATIVE METHODS FOR BUSINESS RESEARCH, ANALYSIS, AND FORECASTING
Prof. Peter Tryfos
Introduction Text Topics Computing Grading Addresses
The course provides a survey of the principal quantitative methods for research, analysis, and forecasting. By "research" we understand the gathering of business information by means of samples and experiments; by "analysis," the detection of patterns, relationships and trends, and by "forecasting," the projection of these patterns, relationships and trends.
Problems of research, analysis, and forecasting arise in all areas of business, but the methods themselves tend to be common for all. The jobs appearing in this page are not current, but they give an idea of the need for analytical skills in a variety of business areas.
The course emphasizes the assumptions, conclusions, and applications of the methods rather than their mathematical derivation or calculation. Applications are illustrated by means of substantial cases and simulations. The cases are drawn mainly from the Canadian environment, and refer to such diverse issues and problems as insurance, real estate, employment equity, credit screening, market research, energy management, demographics, and others. The simulations are cast in the form of business games in which the effect of decisions on profit can be immediately ascertained.
In this era of the computer, methods are rarely implemented by hand, especially in view of the large data sets typically encountered in business studies. The number of computer programs assisting business analysis is rapidly growing and their quality and "friendliness" continually improving. Instruction will be provided for the use of Excel and SAS, programs capable of implementing all the methods surveyed in the course, but students are free to use any other program they prefer.
Because the course examines methods with applications in all areas of business, it may be of interest to students concentrating in a functional area (finance, marketing, etc.), as well as to those concentrating in Management Science.
Peter Tryfos, Methods for Business Analysis and Forecasting: Text and Cases, John Wiley & Sons, 1998, and special supplements.
(Approximate number of weeks in parentheses. Text references in brackets.)
1 Sources of information for business analysis and forecasting: surveys, samples, experiments and time series. Exact and approximate relationships. Estimation of relationships among variables. (0.5) [Ch. 1]
2 Regression. The classical linear model and inferences therefrom. (1.5) [Chs. 2 and 3]
3 Attributes as explanatory variables. Analysis of variance. (1) [Ch. 4]
4 Non-linear relationships. Non-linear regression. (1) [Ch. 5]
5 Regression and time series. Estimation of trend and seasonality. Seasonal adjustment. Autoregression. (1) [Ch. 6]
6 Lagged explanatory variables. Distributed lag models. (1) [Ch. 7]
7 Regression subject to constraints. Least absolute deviations method. Stepwise, all-subsets regression. Elements of experimental design. (1) [Ch. 8]
8 Violations of the classical linear model and simple remedies for non-linearity, non-constant variance, and non-independence. ARIMA models. Specification errors. (1) [Chs. 9 and 10]
9 Attributes as dependent variables. The classification problem. Probability models. Discriminant analysis. (2) [Chs. 11 and 12]
10 Other multivariate methods in brief: factor and cluster analysis. Estimation methods for systems of equations.(1.5) [Chs. 13, and supplements]
11 Sample design. Simple, stratified and two-stage sampling. Estimation methods. (1.5) [Supplement]
Students are free to use whatever software package they prefer. However, support is provided only for Excel and SAS. The use of these programs is illustrated in parallel with the lectures and cases. Excel and SAS may be used to implement all the methods described in the course, to assist the case analyses, and to execute course projects. No previous knowledge of computer programming is required.
The student's grade will depend on two case analyses (weight 40%), a midterm (weight 25%), and a final exam (weight 35%).
The cases are included in the course text. A written analysis of about five pages for each, excluding appendices and computer output, is expected. In addition, students are expected to present in class their analysis of one of the two cases. The intent is to hear and discuss in class one case per week. The schedule of the cases and the random assignment of students to cases will be detailed in the second week of the course.
The midterm and final exams are open-book.
In place of the final exam, students may write a paper involving an original and interesting application of analytical methods to a real problem of their choice. The project must be approved by the instructor. The paper should be typewritten, 10-15 pages long excluding appendixes and computer output, and submitted by the last day of classes.
|Instructor ||Secretary |
|Prof. Peter Tryfos||Ms. Paula Ironi |
|335 SSB||344 SSB |
|Tel.: (416) 736-2100 Ext. 77949||(416) 736-5074 |
|E-mail: firstname.lastname@example.org |