KENNESAW STATE UNIVERSITY
GRADUATE COURSE PROPOSAL OR REVISION,
Cover Sheet (24/01/2013)
Course Number/Program Name MATH 8010 The Theory of Linear Models
Department Mathematics and Statistics
Degree Title (if applicable) Ph.D. in Analytics and Data Science
Proposed Effective Date Fall 2014
Check one or more of the following and complete the appropriate sections:
X New Course Proposal
Course Title Change
Course Number Change
Course Credit Change
Course Prerequisite Change
Course Description Change
Sections to be Completed
II, III, IV, V, VII
I, II, III
I, II, III
I, II, III
I, II, III
I, II, III
Notes:
If proposed changes to an existing course are substantial (credit hours, title, and description), a
new course with a new number should be proposed.
A new Course Proposal (Sections II, III, IV, V, VII) is required for each new course proposed as
part of a new program. Current catalog information (Section I) is required for each
existing course incorporated into the program.
Minor changes to a course can use the simplified E-Z Course Change Form.
Submitted by:
Faculty Member
Approved
_____
Date
Not Approved
Department Curriculum Committee Date
Approved
Approved
Approved
Approved
Approved
Approved
Not Approved
Department Chair
Date
College Curriculum Committee
Date
College Dean
Date
GPCC Chair
Date
Dean, Graduate College
Date
Not Approved
Not Approved
Not Approved
Not Approved
Not Approved
Vice President for Academic Affairs Date
Approved
Not Approved
President
Date
KENNESAW STATE UNIVERSITY
GRADUATE COURSE/CONCENTRATION/PROGRAM CHANGE
I.
Current Information (Fill in for changes)
Page Number in Current Catalog
___
Course Prefix and Number
___
Course Title
___
Class Hours
____Laboratory Hours_______Credit Hours________
Prerequisites
___
Description (or Current Degree Requirements)
II.
Proposed Information (Fill in for changes and new courses)
Course Prefix and Number _MATH 8010_________
Course Title _The Theory of Linear Models________
Class Hours 3 ____Laboratory Hours_0____CreditHours___3 ___
Prerequisites STAT 8210 or equivalent
Description (or Proposed Degree Requirements):
This course provides a solid foundation of the theory behind linear statistical models for
continuous responses. Students will learn to conceptualize linear statistical models using matrix
algebra. The course begins with a review of linear algebra, probability theory, the multivariate
normal distribution, and quadratic forms. Topics will include but not be limited to: simple and
multiple regression, parameter estimation and interpretation, hypothesis testing, prediction,
model diagnostics, model comparison, and variable selection.
III.
Justification
This new course will serve as an elective in the MSAS program and as a requirement in
the Ph.D. in Analytics and Data Science. Students with a particular emphasis in linear
modeling of large data sets are the intended audience.
IV.
Additional Information (for New Courses only)
Instructor: TBD
Text: A First Course in Linear Model Theory by Nalini Ravishanker, Dipak
Dey
Prerequisites: None
Objectives:
Enable the mastering of the mathematical foundations necessary to conceptualize and use
linear statistical models needed to handle large data sets.
Instructional Method:
Traditional in-class instruction.
Method of Evaluation
Exams, a midterm and a final.
V.
Resources and Funding Required (New Courses only)
Resource
Amount
Faculty
Other Personnel
Equipment
Supplies
Travel
New Books
New Journals
Other (Specify)
NA
NA
NA
NA
NA
NA
NA
NA
TOTAL
NA
Funding Required Beyond
Normal Departmental Growth
NA
VI. COURSE MASTER FORM
This form will be completed by the requesting department and will be sent to the Office of the
Registrar once the course has been approved by the Office of the President.
The form is required for all new courses.
DISCIPLINE
COURSE NUMBER
COURSE TITLE FOR LABEL
(Note: Limit 30 spaces)
CLASS-LAB-CREDIT HOURS
Approval, Effective Term
Grades Allowed (Regular or S/U)
If course used to satisfy CPC, what areas?
Learning Support Programs courses which are
required as prerequisites
Mathematics
MATH 8010
The Theory of Linear Models
3–0–3
Fall 2014
Regular
NA
NA
APPROVED:
________________________________________________
Vice President for Academic Affairs or Designee __
VII Attach Syllabus (Attached)
Course:
Instructor:
Office:
Office Hours:
Email:
MATH8010 - The Theory of Linear Models
TBD
TBD
TBD
TBD
Course Pre-requisite: STAT 8210 or equivalent
Course Text:
A First Course in Linear Model Theory by Nalini Ravishanker, Dipak Dey
Meeting Schedule:
TBD
Course Description:
This course provides a solid foundation of the theory behind linear statistical models for
continuous responses. Students will learn to conceptualize linear statistical models using matrix
algebra. The course begins with a review of linear algebra, probability theory, the multivariate
normal distribution, and quadratic forms. Topics will include but not be limited to: simple and
multiple regression, parameter estimation and interpretation, hypothesis testing, prediction,
model diagnostics, model comparison, and variable selection.
Learning Outcomes:
By the end of the course, students should be able to:
1. Demonstrate the mastery of the matrix algebra necessary to facilitate work with linear
models.
2. Demonstrate the mastery of the mathematics necessary to understand various regression
models including but not limited to:
a. Least squares.
b. Generalized least squares.
c. Gauss-Markov theorem.
d. Method of Lagrange multipliers.
e. Multivariate normal and related distributions.
3. Identify issues related parameter estimation and interpretation such as:
a. General linear hypotheses.
b. Confidence intervals and multiple comparisons.
c. Restricted and reduced models.
d. Likelihood based approaches.
4. Understand how to select the correct regression model.
5. Understand the techniques used in regression diagnostics including but not limited to:
a. Properties of the projection matrix.
b. Types of residuals.
c. Outliers and high leverage observations.
d. Diagnostics measures based on influence functions.
6. Demonstrate proficiency in mathematical communication in both oral and written form.
Grading will consist of four components:
Grading:
(2 tests 20% each)
Midterm Exam
Final Exam
40%
30%
30%
Attendance & Assignment Policies: While attendance will not be taken, you are expected to
attend every class period. You are responsible for any material that you miss due to an absence.
Withdrawal Policy…The last day to withdraw from the course and possibly receive a "W"
is MMDDYYYY.
Students who find that they cannot continue in college for the entire semester after being
enrolled, because of illness or any other reason, need to complete an online form. To completely
or partially withdraw from classes at KSU, a student must withdraw online at
www.kennesaw.edu, under Owl Express, Student Services.
The date the withdrawal is submitted online will be considered the official KSU withdrawal date
which will be used in the calculation of any tuition refund or refund to Federal student aid and/or
HOPE scholarship programs. It is advisable to print the final page of the withdrawal for your
records. Withdrawals submitted online prior to midnight on the last day to withdraw without
academic penalty will receive a “W” grade. Withdrawals after midnight will receive a “WF”.
Failure to complete the online withdrawal process will produce no withdrawal from classes. Call
the Registrar’s Office at 770-423-6200 during business hours if assistance is needed.
Students may, by means of the same online withdrawal and with the approval of the university
Dean, withdraw from individual courses while retaining other courses on their schedules. This
option may be exercised up until MMDDYYYY.
This is the date to withdraw without academic penalty for SEMESTER YYYY classes. Failure to
withdraw by the date above will mean that the student has elected to receive the final grade(s)
earned in the course(s). The only exception to those withdrawal regulations will be for those
instances that involve unusual and fully documented circumstances.
Academic Integrity: Every KSU student is responsible for upholding the provisions of the
Student Code of Conduct, as published in the Undergraduate and Graduate Catalogs. Section II
of the Student Code of Conduct addresses the University’s policy on academic honesty,
including provisions regarding plagiarism and cheating, unauthorized access to University
materials, misrepresentation/falsification of University records or academic work, malicious
removal, retention, or destruction of library materials, malicious/intentional misuse of computer
facilities and/or services, and misuse of student identification cards. Incidents of alleged
academic misconduct will be handled through the established procedures of the University
Judiciary Program, which includes either an “informal” resolution by a faculty member, resulting
in a grade adjustment, or a formal hearing procedure, which may subject a student to the Code of
Conduct’s minimal one semester suspension requirement.
Unauthorized Collaboration: Submission for academic credit of a work product, or a part
thereof, represented as its being one's own effort, which has been developed in substantial
collaboration with or without assistance from another person or source, is a violation of
academic honesty. It is also a violation of academic honesty knowingly to provide such
assistance. Collaborative work specifically authorized by a faculty member is allowed.