to provide
students with a perspective on the integration of the functional areas of
business, while maximizing the use of
analytical skills
and knowledge
for decision making in a contemporary global business environment
DEPARTMENT OF BCIS/QM
Tues,
6:00–8:20 P.M. in Breslin 205.
Instructor: Dr.
Lonnie K. Stevans Department: BCIS/QM
Office Hours: Tuesday, 5:00P.M.-6:00P.M. Chairperson: Dr. John Affisco
Phone: (516) 463-5375 Phone: (516)
463-5716
Home Phone: (631)
598-8518
Email: acslks@hofstra.edu
DESCRIPTION OF COURSE: Regression modeling, analysis of variance, time series
analysis and business forecasting methods and nonparametric methods. Use of
statistical packages.
PREREQUISITES OF COURSE: Statistics for Business Applications
Residency Workshop or approved equivalent.
REQUIRED TEXT: “A Second Course in
Statistics: Regression Analysis”, Mendenhall and Sincich. Sixth Edition,
Pearson/Prentice Hall.
REQUIRED SOFTWARE: Minitab 14 Student Version, http://www.journeyed.com/itemDetail.asp?T1=17821689N
COMPUTER PROFICIENCY REQUIRED: Familiarity with the Windows Operating System
OUTCOME OBJECTIVES AND METHODS OF ACHIEVING THE OBJECTIVES
The course will provide participants with a
comprehensive introduction to linear modeling tools. Emphasis will be placed on the modeling
process, including variable transformations and validation issues, as well as
the use and interpretation of the linear model.
All modeling will be undertaken using Minitab software procedures.
ATTENDANCE POLICY
Students
are strongly advised to attend every class. Students who miss a class are
responsible for all work, handouts and assignments given out, or collected in,
that class. Attendance alone has no
impact on final grades.
METHODS OF EVALUATING STUDENT
Final
grades are determined in the following way.
Homework 15%
Midterm 40%
Final (Project) 45%
Most
of the homework will involve the use of Minitab and must be handed in on time. Late
homework will not be accepted. All work is expected to be your own. Cases of plagiarism will be dealt with in
accordance with the University disciplinary policy.
Only
basic calculators that are capable of doing only simple arithmetic functions
will be permitted during examinations.
If students desire to use a calculator during an examination, they are
responsible for providing the appropriate model, and it must be removed from
its case.
To
be eligible for a makeup examination, a student must submit to the instructor
written documentation of the reason for missing a scheduled examination due to
medical problems or death of an immediate family member. The instructor (not
the student) determines whether and when a makeup is to be given. If a
makeup examination is to be given, the instructor will determine the type of
makeup examination. If the student misses (for any reason) the scheduled makeup
examination, additional make-ups are not permissible.
UNIVERSITY POLICY ON INCOMPLETE GRADES
An Incomplete grade will be given at the discretion
of the instructor in a graduate course and only under unusual circumstances.
Incomplete work must be completed and submitted to the instructor for a grade
by the end of one calendar year from the close of the semester or session in
which the course was taken.
|
Topic |
Content |
Introduction
Chapter 1&2 |
Conceptual
Overview of Statistical Models. Brief
Introduction to Minitab
|
Simple
Regression
Chapter 3 |
The Simple Regression Model Estimation Inference: t-test Assessing Fit and Correlation Prediction and Interpretation Other Comments |
Multiple RegressionChapter 4 |
Multiple Regression Model Fitting Using SAS Inference: Global F-Test Interpretation and Prediction Variable Transformations Nested Model Testing |
|
|
Midterm |
|
Chapter 5 |
Quantitative Variable Modeling Qualitative Variable Modeling Mixed Models |
|
Stepwise
Regression and Some
Cautionary Tales Chapters 6&7 |
Overview of Stepwise Methods Deviation from Assumptions Parameter Estimability Multicollinearity Extrapolation |
|
Residual Analysis Chapter 8 |
Plotting Residuals Detecting Heteroscedacity Checking Normality Assumption Outlier Detection |
|
Time Series Modeling Chapter 10 |
Regression Time series Modeling Autocorrelation |
|
Logistic Regression Chapter 9.5 & 9.6
(p.445) |
Logistic Regression |
|
Review |
|
|
|
Final |