KENNESAW STATE UNIVERSITY
GRADUATE PROGRAM/CONCENTRATION PROPOSAL
FORM 02/25/04
PROGRAM OR CONCENTRATION NAME: Master of Science in Applied Statistics
DEPARTMENT: Mathematics and Statistics
PROPOSED EFFECTIVE DATE:August 1, 2011
Check One or More of the Following and Complete the Appropriate Sections
Sections to be Completed
_____New Program Proposal**
All
__X__Change in Program/Concentration/Degree Requirements
III – VII, XII
_____New Concentration Proposal
I – VII, XII
**A new course proposal is required for each new course that is part of the new program
Submitted by:
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Faculty Member
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Department Curriculum Committee
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___ Approved ___ Not Approved
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Department Chair
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College Curriculum Committee
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College Dean
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GPCC Chair
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___ Approved ___ Not Approved
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Dean, Graduate Collelge
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___ Approved ___ Not Approved
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Vice President for Academic Affairs
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___ Approved ___ Not Approved
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President
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UNIVERSITY SYSTEM OF GEORGIA
NEW GRADUATE PROGRAM PROPOSAL
CHANGE
Master of Science in Applied Statistics
Kennesaw State University
August 20, 2010
Department of Mathematics and Statistics
College of Science and Mathematics
Proposed Start Date:
CIP:______
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I.
Program Description
Unique Aspects of Program
Institutional Importance of the Program
KSU's Strategic Plan
System and State of Georgia Goals
Staffing, Facilities and Enrollment
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II Objectives of the Program
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III Justification and Need for the Program (Change)
When the Master of Science in Applied Statistics was first designed, emphasis was placed on
process improvement and business/industry applications. Three of the six statisticians on the
faculty had training and experience in these areas, so the program was designed to take
advantage of those strengths. As the faculty has grown and added more areas of expertise and as
we realize student interests do not match the original design of the program, we decided to make
programmatic changes to reflect the faculty expertise and student interests.
Since the initial design of the program, we have added faculty members with expertise in
biostatics, engineering and educational psychology. Our students have expressed interest in
more courses that will prepare them for employment and further graduate study in public health
and epidemiology. Our advisory board has told us that courses in time series analysis and binary
classification would be helpful for students seeking employment in the banking or credit
industries.
Because of the reasons mentioned above, we have decided to add several courses and restructure
the program to better accommodate student and employer interests, while maintaining our desire
to give our students a broad, general exposure to applied statistics.
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IV Procedures Used to Develop the Program (Change)
Interest in making the program changes began with advice from members of our advisory board
based on their experiences with our graduates and graduates from other programs, particularly
from Georgia Tech. The advisory board thought students ought to graduate with more advanced
SAS skills and some experience in working with large data sets (hundreds of millions to a billion
data values).
We developed Advanced SAS and Binary Classification courses, which were taught as Special
Topics courses, to address the concerns of the advisory board. The courses were well received
and students reported that having taken the courses helped in job interviews. Employers were
impressed with the SAS and real data experiences.
Some students expressed interest in pursuing employment or graduate school in public health.
We developed courses in the Design of Human Studies and Longitudinal Data Analysis and
taught them as Special Topics to satisfy those students. These courses were also well received
by students.
After teaching the special topics courses, we discussed how to modify the program to include
these courses as options. We wanted to give the students the opportunity to include some of
these courses in their programs of study, but we also wanted to preserve the general applied
nature of the program. The changes described below are the result of the discussion.
Time Series Analysis is the only course that has not been offered as a Special Topic. Two
faculty members have expertise in time series modeling, but they are needed to teach other
courses in the MSAS program, as well as courses in the Applied Statistics and Data Analysis
minor and the Mathematics major.
We have already hired one new faculty member, and expect to hire two more this year. In
addition, Dr. Michele Pietro, the new Executive Director of CETL, is a statistician, and he will
teach courses in the MSAS program. With these new faculty members, we will be able to offer
the new courses on a regular basis.
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V Curriculum: Degree Program Requirements
MSAS Program Requirements Prior to Proposed Changes
Ten Courses Needed for Graduation:
Stat 7010 – Mathematical Statistics I
Stat 7020 – Statistical Computing
Stat 7100 – Statistical Methods
Stat 8110 – Quality Control and Process Improvement
Stat 8120 – Applied Experimental Design
Stat 8140 – Six Sigma Problem Solving
Stat 8210 – Applied Regression Analysis
Stat 8240 – Data Mining
Stat 8310 – Applied Categorical Data Analysis
Stat 8320 – Applied Multivariate Data Analysis
Six hours of Stat 8940 – Applied Analysis Project – and a project report are required.
Up to nine hours may be substituted with the permission of the Program Director.
Program Admission Requirements:
Unchanged
MSAS Program Requirements After Proposed Changes
Ten Courses Needed for Graduation:
I – The following four courses are required of every student:
Stat 7020 – Statistical Computing
Stat 7100 – Statistical Methods
Stat 7010 – Mathematical Statistics I
Stat 8210 – Applied Regression Analysis
II - All students are required to take at least one of the following two courses:
Stat 8120 – Applied Experimental Design
Stat 8125 – Design of Human Studies
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III - All students must take at least two of the following six courses:
Stat 8120 – Applied Experimental Design (if not taken in Section II)
Stat 8125 – Design of Human Studies (if not taken in Section II)
Stat 8220 – Time Series Analysis
Stat 8225 – Applied Longitudinal Data Analysis
Stat 8240 – Data Mining
Stat 8310 – Applied Categorical Data Analysis
Stat 8320 – Applied Multivariate Data Analysis
Stat 8330 – Applied Topics in Binary Classification
IV - The following courses may be used to complete the degree requirements:
Stat 8020 – Advanced Programming in SAS
Stat 8110 – Quality Control and Process Improvement
Stat 8140 – Six Sigma Problem Solving
Six hours of Stat 8940 – Applied Analysis Project – and a project report are required.
Up to nine hours may be substituted with the permission of the Program Director.
Course Offering Schedule and Plan-of-Study
Each required course (Group I) will be offered twice per academic year. The courses in Group II
(Stat 8120 and 8125) will be offered three times in two academic years. The courses in Groups
III and IV will be offered at least once per academic year.
Course Descriptions
STAT 8020. Advanced Programming in SAS. 3-0-3
Prerequisite: Stat 7020 and Stat 7100
This course will cover advanced programming techniques using the SAS system for data
management and statistical analysis. The topics covered include macro programming, using
SQL with SAS and optimizing SAS programs. Upon completion of this course students will be
prepared to take and pass the certification test and obtain the Advanced Programmer for SAS 9
certification.
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STAT 8125. Design and Analysis of Human Studies. 3-0-3
Prerequisite: Stat 8210
This course will serve as an introduction to epidemiologic methods used to investigate disease
outbreaks and the effectiveness of public health interventions. At the end of the course, students
will be able to design, analyze, and report the results of an epidemiologic investigation and will
be able to interpret literature related to analysis of studies of disease causality and treatment.
STAT 8220. Time Series Forecasting. 3-0-3
Prerequisite: Stat 8210
This course provides an introduction to univariate time-series analysis that emphasizes the
practical aspects most needed by practitioners and applied researchers. Topics covered include
linear regression applied to time series, simple autoregressive models (ARMA and ARIMA), and
Box-Jenkins methodology.
STAT 8225. Applied Longitudinal Data Analysis. 3-0-3
Prerequisite: Stat 8210
This course introduces students to methods of longitudinal data analysis and issues involved with
the analysis of repeated measures data. The course will be based on multilevel models (also
referred to as hierarchical models, mixed effects models, and random coefficient models) with a
major emphasis on modeling intraindividual effects as a precursor to modeling interindividual
effects. Students will learn how to choose an appropriate model so that specific research
questions of interest can be addressed in a methodologically sound way.
STAT 8330. Applied Binary Classification. 3-0-3
Prerequisite: Stat 8210
Binary Classification is a heavily used concept in statistical modeling. Common applications
include credit worthiness and the associated development of a “FICO-esque” credit score, fraud
detection or the identification of manufacturing units which fail inspection. Students will learn
how to use logistic regression, odds, ROC curves, maximization functions to apply binary
classification concepts to real-world datasets. This course will heavily use SAS software and
students are expected to have a strong working knowledge of SAS.
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VI INVENTORY OF FACULTY DIRECTLY INVOLVED
The statistics faculty of the Department of Mathematics and Statistics will be involved:
Dr. Bradley Barney – beginning in Fall 2011
Dr. Marla Bell
Dr. Michele DiPietro
Dr. Anda Gadidov
Dr. Victor Kane
Dr. Louise Lawson
Dr. Sherry Ni
Dr. Jennifer Priestley
Dr. Lewis VanBrackle
Dr. Daniel Yanosky
Plus two additional statisticians to be hired in the 2010-2011 academic year
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VII Outstanding programs of this nature at other institutions
Most graduate degrees in statistics have versions of the times series and longitudinal data
analysis courses we have added to our program. Examples are the programs at Virginia Tech,
North Carolina State University and the Florida State University.
The material in the courses on advanced SAS programming and binary classification are not as
commonly found in individual statistics courses in other universities. The advanced SAS
programming skills are more often learned in advanced data mining courses (as at the University
of Alabama and the University of Central Florida). The binary classification course material is
more often found in courses on logistic regression (as at Florida State University) or advanced
regression (as at Virginia Tech). Our course differs from others in its emphasis on applications
using real, large, messy datasets.
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VIII Inventory of pertinent library resources
IX Facilities
X Administration
XI Assessment
XII Accreditation
Business Content Audit:
Referencing the Business Content Worksheet, does this program or concentration, incorporating
the information proposed herein, have “traditional business subject” content delivered by faculty
or programs administered by the Coles College of Business? *
_____ Yes
If “yes,” complete a Business Content Worksheet and obtain necessary
approvals as indicated.
__X___ No
_____ Exempt Graduate programs administered by the Coles College are exempt.
__________
*
All graduate programs exceeding 50% business content are automatically required to meet AACSB
International business accreditation standards. The purpose of the audit is to prevent the unintended,
undesirable compulsory inclusion of programs subject to AACSB standards.
XIII Affirmative Action Impact
XIV Degree Inscription
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XV Fiscal and Enrollment Impact, and Estimated Budget
1. ENROLLMENT PROJECTIONS
2. COSTS
A. Personnel -- reassigned or existing positions
B. Personnel -- new positions
C. One-Time Startup costs
D. Operating Costs
3. REVENUE SOURCES
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