last updated on 2/4/02 at 13:20:16


For good reference and quick and clean description of statistical concepts you may take a look at the following Schaum's books:


In addition to MBA standing, there are two very strict prerequisites for this course: First: you must have completed a course in introductory statistics roughly covering chapters 2-5 and 7-9. This material will be very quickly reviewed in the first two lectures, but the purpose is solely to refresh your memory. Second: you should be fairly comfortable with Excel (97 or 2000). You don't have to be experts; you won't be required to write VBA macros and such, however you are required to know the basic operations of Excel.

Required work, homeworks and grading policy

  1. There will be five or six homework assignments and doing them is optional. Students who choose to hand any of the assignments, they will be graded and the score will be counted as 10% of their final grade for each assignment handed in.

  2. There will be two exams, each worth 1/2 of the percentage left over from the assignments (therefore if a student chooses to hand in four of the assignments, they will constitute 40% of her grade, then the two exams will count as 30% of her grade each.

Warning: The exams are going to be very closely related to homeworks, so you are urged to work on all the homeworks, whether you decide to hand them in or not. If you choose not to hand in any assignments it means that each exam will count 50% of your grade. This is extremely risky and will make your exams somewhat of a do or die situation.

Topics In Brief

Topic 1: Review of basic probability and statistics (two weeks)
Topic 2: Regression analysis and inference (four weeks)
Topic 3: Time series analysis (one week)
Topic 4: Optimization techniques and models (three weeks)
Topic 5: Simulation modeling (two weeks)

Topics In Detail and reading assignments:

Topic 1 Weeks of 1/28 and 2/4: Review of basic Probability and statistics: In the first two weeks we review several notions from probability and statistics that students are expected to have learned already. Emphasis will based on using spreadsheets to do calculations.
Topic 2 Weeks of 2/11, 2/18, 2/25 & 3/4: Regression analysis and inference:

Topic 3 Week of 3/11: Time series analysis: evolution of data over time, random series, random walks models, regression and time series: trend lines, smoothing techniques: moving averages and exponential smoothers, Holt's method, dealing with seasonality. Reading chapter 13.

Mid-Term Exam Week of 3/11

Spring Break Week of 3/18

Topic 4 Weeks of 3/25, 4/1 & 4/8: Optimization techniques and modeling:

Topic 5 Week of 4/22: Simulation modeling: random numbers and random number generators, using random number generators to simulate possible random outcomes, using random number generators to produce various statistical distributions, Applications in projecting future income and revenue and other financial planning, simulating stocks and options prices. Reading: Chapter 16

Final Exam Week of 4/29