COURSE SYLLABUS
MBST-600
MANAGERIAL DECISION ANALYSIS
Fall, 2003
Class meets:
Wednesday 5:30-8:30 p.m., MCN 325
Instructor: Dr. Arkady Shemyakin
Office OSS 221 , tel. 962-5522, a9shemyakin@stthomas.edu
Office hours: M 2-5 p.m., W 4-5 p.m., R 5:30-7:30 p.m.,
and by appointment
Textbook:
Bowerman & O’Connell
BUSINESS STATISTICS IN PRACTICE
(referred below as BOH, see specific pages and sections)
Third edition.
Supplementary Material:
MINITAB FOR WINDOWS, STUDENT EDITION RELEASE 12
A PRIMER FOR MBST 500/600
Copyright 2001, QMCS Dept., UST
(referred below as MW),
September 3
10
17
24
October
1
8
15
Introduction.
Review: Chapters 1,2,4-6. Descriptive Statistics
[BOH pp.3-120, 153-254, MW pp.1-22]
Review: Chapters 7-8. Inferential Statistics
[BOH pp.255-282, 301-332, MW pp.23-24, 27-28, 42]
Chapter 9. Two Sample Problems
[BOH pp. 353-395, MW pp.25-26, 28]
Assignment #1 handed out
Chapter 10. Experimental Design and Analysis of Variance
[BOH pp.399-440, MW p.30]
Chapter 11. Simple Linear Regression
[BOH pp.445-500, 519-520, MW pp.31-34]
Assignment #1 (Ch.9-10) due. Assignment #2 handed out
Chapter 12. Multiple Regression and Model Building
[BOH pp.525-618**, MW pp.35-41]
Assignment #2 (Ch. 11-12) due
Review
Group Project Discussion
EXAM #1
22
29
November 5
12
19
December
3
9 (no class!)
10
Chapter 13*. Time Series. Forecasting
[BOH pp.625-653*]
Chapter 14. Statistical Process Control
[BOH pp.667-714*]
Managerial Decisions and Quality Management
Assignment #3 handed out
Chapter 16. Chi-Square Tests
[BOH pp.757-779]
Chapter 15*. Nonparametric Methods
[BOH pp.728-753*]
Presentation of Cases for Individual Course Papers
Review: Chapter 3. Probability
[BOH pp.121-152]
Decision Making under Uncertainty
(problem solving session)
Assignment #3(Ch. 13-16) is due
Assignment #4 handed out
Chapter 17. Decision Theory
[BOH pp.783-805]
Decision Making under Risk (problem solving session)
Assignment #4 (Ch. 3,17 ) is due
Group Projects Presentations
Course Evaluation and Review
Individual Course Papers Due
Exam #2 (Discussion of Course Papers)
Exact times, pages and sections to be covered are subject to further changes. Some
further omissions are possible*. Course materials will be posted on UST Blackboard
class site regularly a few days ahead.
TESTING AND GRADING POLICY:
Home assignments #1-#4 are handed out and posted on the Blackboard one or two weeks
before the due date. All four assignments are based on the textbook material. They will
require the use of statistical formulas, ability to interpret computer outputs, and apply
computer software (MINITAB) to data analysis. Late assignments will not be accepted,
because the assignment discussion follows in class and on the Blackboard, and solutions
are posted directly after the due date.
Group Project. Find partners to work together in a small group (3 to 5 students in each
group) no later than October 8. Select a problem of your choice. It could be a job related
or a general interest situation. Demonstrate how the methods of data analysis you learn in
this class can be applied to making managerial decisions. The results will be presented in
class December 3 (15 minutes for each group presentation followed by a general
discussion). Written group project papers (5-10 pages including attachments) should be
submitted to the instructor the same day.
Group project presentations will be evaluated by peer review. Instructor will judge the
discussion participation and papers.
Exam #1 is a two-hour open-book in-class test covering material of the first half of the
course (September 3 to October 8). It is designed to test statistical techniques and ability
to interpret computer outputs of the data analysis.
Exam #2 (Individual Course Paper) is an individual assignment covering material of the
course emphasizing the second half (October 9 to November 19). It will test the students’
ability to analyze raw data with the help of computer software, to assess uncertainty, and
to make managerial decisions based on data analysis. Three different cases will be
presented for the students’ choice in class and posted on the Blackboard November 19.
The paper (5 to 10 pages including attachments) is to be submitted electronically or on
paper no later than December 9. Prior discussion with the instructor is encouraged.
Individual 10-minute discussions with the instructor will be scheduled for final class
meeting December 10.
Grading. Four home assignments are graded out of 50 points (5% of the total grade)
each. Group project is worth 300 points (20% of the total grade), 300 points (30% of the
total grade) each are awarded for Exam #1, 200 points – for Exam #2.
50x4+300+300+200=1000 (maximum). Some extra credit opportunities will be provided.
Normally, 900 points (90% of the total) will give you an “A”, 750 – a “B”, 600 – a “C”
(passing). Grades of “A-“ and “B+” are awarded by the instructors’ discretion to the
students achieving the range of 800 to 900 points, grades “B-“ and “C+” to the students
in the range of 650 to 700 points.
PREREQUISITES (TOPICS COVERED IN MBST-500)
Overview of the process of collecting data.
Concepts of measurement: validity, reliability, accuracy, precision.
Scales of measurement: nominal, ordinal, interval, ratio.
Population vs. sample.
Randomization, sampling design, simple random sample, variability.
Bias in statistical data.
Sources of measurement error: non-sampling and sampling.
Experimental vs. Observational Studies: Implications for imputing causality.
Descriptive Statistics - One variable:
Graphical: histogram, frequency polygon, bar chart, pie chart, box and whisker
plot, skewness.
Numerical: mean, median, mode, range, standard deviation.
Descriptive Statistics - Two variables:
Graphical: cross-tabulation, box and whisker plots (on a common base), scatterplot,
regression line.
Numerical: regression equation (minimal knowledge required)
Probability:
the concept of probability, statistical independence (intuitive
understanding)
Probability Distributions: discrete (binomial), continuous (normal), expected value,
standard deviation.
Sampling distributions: shape, mean, standard deviation, effect of sample size.
Central Limit Theorem.
Inferential Statistics: An introduction.
Parameter Estimation: point estimate, interval estimate, confidence level, error of
estimate, effect of sample size, estimate of a mean and a proportion.
Hypothesis Testing: type I and type II errors, alpha and beta, test of a mean and a
proportion.
NOTE: All students registering for MBST-600 Decision Analyses should have an
understanding of the topics listed above. In addition, all students registering for MBST600 are expected to have a working knowledge of a statistical package sufficient to
produce appropriate graphs and statistics for the above topics. The recommended
statistical package is MINITAB, but other packages are acceptable if they are capable of
doing multiple regression, ANOVA, etc. Note that EXCEL and LOTUS are not capable
of performing some of the statistical techniques required in MBST-600.
MINITAB KNOWLEDGE REQUIRED FOR ENTERING MBST-600
THE TOPICS OVER WHICH YOU SHOULD HAVE MASTERY
WHEN ENTERING MBST-600 ARE AS FOLLOWS:
You should know the material on the following pages of MINITAB FOR WINDOWS,
STUDENT EDITION RELEASE 12, A PRIMER FOR MBST 500 / 600 AND OTHER
DATA ANALYSTS ( COPYRIGHT 2001, QMCS DEPT., UNIVERSITY OF ST.
THOMAS):
Pages 1-24, 27, 42.
If you use a statistical package other than MINITAB:
If you do not use MINITAB, consult a student edition of MINITAB and make sure that
you can work with the topics listed above. You should be aware that some statistical
packages are not capable of performing some of the more advanced techniques you will
encounter in MBST-600. Note that EXCEL and LOTUS are not capable of performing
some of the statistical techniques required in MBST-600.