ISOM 253
A Second Course in Business Statistics
Instructor
Inchi Hu, Professor of ISOM, Room 4436, Tel: 2358-7734
Email: imichu@ust.hk;
Office hours: By appointment
Tutorials
The weekly tutorial will go over assignments and supplementary
materials. The tutorials will begin in the second week.
Tutor: Mr. Cap Leung
Email:capleung@ust.hk
Homepage: http://lmes2.ust.hk
All lecture notes, assignments, solutions, old exam papers, and
important announcements will be posted on the homepage
Course Objective
The objective of the course is to equip students with
theoretical knowledge as well as practical skills in modern
regression analysis so that the regression method becomes an
important tool in solving various business problems. The course
consists of four parts: I. Background Knowledge; II. Multiple
Regression Methods; III. Case Studies; IV. Special Topics in
Regression.
Intended Learning Outcomes
Upon completion of the course, you should be able to
¾ Understand the basic meaning of regression and determine
whether regression is the appropriate method to solve a
particular business problem.
¾ Know how to set the stage for regression analysis such as
obtaining the relevant data and running regression using
statistical software.
¾ Interpret computer output from regression analysis and
detect potential problems in the current regression model.
¾ Improve regression models based on regression analysis
results
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Teaching Method
The method is lecturing aided by directed discussion. The
context of the relevant concepts and methods will be presented
first followed by the discussion of pre-designed questions and
examples to explore the concepts and methods in depth.
Course Materials:
¾ “Applied Regression Analysis”, 4rd Edition, by Terry E.
Dielman, published by Duxbury
¾ Additional materials (lecture notes and business cases) will
be available from course website.
Assessment scheme
Your grade is based on the following components:
The 1st exam (35%) covers Parts I and II. It is an in-class,
closed-book, written exam of approximately 90 minutes long.
The 2nd exam (35% ) covers Parts III and IV. It is of similar
format as the first one.
Assignments (30%) will be given every one to two weeks.
There are two types of assignments: individual-based and group
projects, where students work in groups. Each group also needs
to do an oral presentation on their solution to a business case.
All assignments will be collected and returned by your teaching
assistant during tutorial sessions.
Schedule
Part I: Background Knowledge
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Tue, Feb. 2, Setting the stage
Thur, Feb. 4, Introduction & the least squares method
Tue, Feb. 9, the regression model and inference
Thur, Feb. 18, Confidence & prediction intervals
Tue, Feb. 23, Correlation analysis
Thur, Feb. 25, Introduction to multiple regression
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Part II: Multiple regression
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Tue, Mar. 2, Marginal sums of squares
Thur, Mar. 4, Use of variables in multiple regression
Tue, Mar 9, The meaning of regression coefficients
Thur, Mar. 11: Variable selection techniques
Tue, Mar. 16, Model selection in multiple regression
Thur, Mar. 18, Residual analysis
Tue, Mar. 23, Detecting outliers in regression
Thur, Mar. 25, Autocorrelated errors in regression
Tue, Mar. 30, Review for the 1st Examination
Thur, Apr 1, the 1st Examination
Part III: Case Study
z Thur, Apr 8, Lincoln Community Hospital – forecasting
costs by assessing the effects of different factors
z Tue, Apr 13, Flanders of Springfield – direct mail
strategy with time dynamic consideration
z Thur, Apr 15, CFS site selection – using regression for
site selection.
z Tue, Apr 20, Nopane – advertising strategy in a
game-theory setting
Part IV: Selected topics in regression
z Thur, Apr 22, Logistic regression - model and fitting
z Tue, Apr 27, Logistic regression inference & model
selection
z Thur, Apr 29, nonlinear regression
z Tue, May 4, Design of experiments
z Thur, May 6, Analysis of variance
Tue, May 11, Review for the 2nd examination
Thur, May 13, the 2nd examination
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