UCC/UGC/ECCC
Proposal for New Course
Please attach proposed Syllabus in approved university format.
1. Course subject and number: BA 522
2. Units:
See upper and lower division undergraduate course definitions.
3. College:
The W.A. Franke College of
Business
4. Academic Unit:
3
MBA Program
5. Student Learning Outcomes of the new course. (Resources & Examples for Developing Course Learning
Outcomes)
At the end of this course student should be able to:
1. Understand and use appropriately basic statistical terminology such as average, median,
standard deviation, variance, expected value, p-value, type I error, type II error, correlation.
2. Use Excel to perform basic data analysis and graphing, simple linear regression,
correlation, and hypothesis testing.
3. Generate and interpret measures of statistical inference and apply conclusions from the
inference to decision-making in a management context.
4. Demonstrate an understanding and ability to interpret the results of statistical analysis and
apply them to real problems.
5. Understand and describe operations management concepts such as production/service
process matrix, forecasting, quality and quality control, supply chain management, and
inventory control.
6. Perform and evaluate a forecast using introductory time series methods (i.e. moving
average) and linear regression.
7. Develop and use statistical process control charts.
6. Justification for new course, including how the course contributes to degree program outcomes,
or other university requirements / student learning outcomes. (Resources, Examples & Tools for Developing
Effective Program Student Learning Outcomes).
Provides an efficient mechanism for students to acquire knowledge necessary for the MBA
program or for non-MBA or certificate students to gain business foundation knowledge.
Required for the Business Foundations certificate.
7. Effective BEGINNING of what term and year?
See effective dates calendar.
Summer 2013
8. Long course title: Quantitative Analysis and Operations Management for Decision Making
(max 100 characters including spaces)
Effective Fall 2012
9. Short course title: QA and OM for Decision Making
(max. 30 characters including spaces)
10. Catalog course description (max. 60 words, excluding requisites):
Students will review basic data analysis and statistics, and learn basic operations
management concepts such as quality management and quality control, forecasting, supply
chain management, production and service processes, and inventory control all in a context
of managerial decision making.
11. Will this course be part of any plan (major, minor or certificate) or sub plan (emphasis)?
Yes
If yes, include the appropriate plan proposal.
Business Foundations Graduate Certificate
No
12. Does this course duplicate content of existing courses?
Yes
No
If yes, list the courses with duplicate material. If the duplication is greater than 20%, explain why
NAU should establish this course.
Some duplication of undergraduate course content is present, but MBA students and students
seeking increased knowledge of business at the graduate level do not need all the content
offered in the undergraduate courses. The focus in the graduate courses will be more
managerial and less procedural. The pace of material coverage will be accelerated.
13. Will this course impact any other academic unit’s enrollment or plan(s)?
If yes, include a letter of response from each impacted academic unit.
14. Grading option:
Letter grade
Yes
Pass/Fail
No
Both
15. Co-convened with:
14a. UGC approval date*:
(For example: ESE 450 and ESE 550) See co-convening policy.
*Must be approved by UGC before UCC submission, and both course syllabi must be presented.
16. Cross-listed with:
(For example: ES 450 and DIS 450) See cross listing policy.
Please submit a single cross-listed syllabus that will be used for all cross-listed courses.
17. May course be repeated for additional units?
16a. If yes, maximum units allowed?
16b. If yes, may course be repeated for additional units in the same term?
Completion of a math course at least
one level above college algebra, and
18. Prerequisites:
post baccalaureate or graduate status
If prerequisites, include the rationale for the prerequisites.
Effective Fall 2012
Yes
No
Yes
No
Students must have problem solving skills beyond college algebra to succeed in this course.
The content and pace will be suited to students who have completed an undergraduate
degree.
19. Co requisites:
If co requisites, include the rationale for the co requisites.
20. Does this course include combined lecture and lab components?
Yes
If yes, include the units specific to each component in the course description above.
21. Names of the current faculty qualified to teach this course:
No
Susan Williams, Bob Sellani
Answer 22-23 for UCC/ECCC only:
22. Is this course being proposed for Liberal Studies designation?
If yes, include a Liberal Studies proposal and syllabus with this proposal.
Yes
23. Is this course being proposed for Diversity designation?
If yes, include a Diversity proposal and syllabus with this proposal.
Yes
Scott Galland
Reviewed by Curriculum Process Associate
10/9/2012
Date
Approvals:
Department Chair/ Unit Head (if appropriate)
Date
Chair of college curriculum committee
Date
Dean of college
Date
For Committee use only:
UCC/UGC/ECCC Approval
Date
Approved as submitted:
Yes
No
Approved as modified:
Yes
No
Effective Fall 2012
No
No
BA 522 QUANTITATIVE ANALYSIS & OPERATIONS MANAGEMENT FOR DECISION MAKING
COURSE SYLLABUS
General Information
 The W. A. Franke College of Business, MBA Program

BA 522 Quantitative Analysis and Operations Management for Decision Making

Summer only, TBD

3 credit hours Units

Instructor’s name: TBD

Office address: TBD

Office hours: TBD
Course prerequisites:
Completion of a math course at least one level above college algebra, and post baccalaureate or graduate status
Course description:
Students will review basic data analysis and statistics, and learn basic operations management concepts such as
quality management and quality control, forecasting, supply chain management, production and service
processes, and inventory control all in a context of managerial decision making.
Student Learning Expectations/Outcomes for this Course:
At the end of this course student should be able to:
A. Understand and use appropriately basic statistical terminology such as average, median, standard
deviation, variance, expected value, p-value, type I error, type II error, correlation.
B. Use Excel to perform basic data analysis and graphing, simple linear regression, correlation, and
hypothesis testing.
C. Generate and interpret measures of statistical inference and apply conclusions from the inference to
decision-making in a management context.
D. Demonstrate an understanding and ability to interpret the results of statistical analysis and apply them to
real problems.
E. Understand and describe operations management concepts such as production/service process matrix,
forecasting, quality and quality control, supply chain management, and inventory control.
F. Perform and evaluate a forecast using introductory time series methods (i.e. moving average) and linear
regression.
G. Develop and use statistical process control charts.
Course structure/approach:
Course is a combination of reading, short lectures, computer labs, and problem solving. Cases and mini-cases
will be used.
Textbook and required materials:
 Readings from an introductory business statistics textbook (such as Levine, Stephen, Krehbiel and
Berenson, “Statistics for Managers Using Microsoft Excel”) and from a core operations management
textbook (such as Heizer & Render) will be required.
Effective Fall 2012

Software: Students must be required to use Excel in at least one assignment. Other software tools
should be used such as Excel OM or POM (software available with Heizer & Render textbook) and the
statistics tools listed in the optional materials.
Recommended optional materials/references (attach reading list):
 Publishers’ resources such as Prentice Hall’s My OM Lab (can accompany the Heizer & Render text)
may be useful.
 HyperStat at http://davidmlane.com/hyperstat/index.html
 StatSoft at http://www.statsoft.com/textbook/stathome.html
Course outline:
Quantitative analysis module:
1. Descriptive Graphs & Measures
2. Confidence Intervals Estimation
3. Hypothesis Testing
4. Correlation and Simple Linear Regression Analysis
Operations Management module:
1. Total Quality Management & Statistical Control Processes
2. Forecasting
3. Inventory management
4. Aggregate planning
5. Supply chain management
Assessment of Student Learning Outcomes:
The majority of assessment should be individual work.
 Methods of Assessment
o Homework
o Lab assignments
o Lab quizzes
o Lab exams
 Timeline for Assessment
o Homework for most classes
o Lab quizzes every 4-6 classes
o Lab exam – mid-term/final or just final if the lab quizzes substitute for a mid-term
Grading System:
~15% Homework
~15% Lab Assignments
~10% Participation
~30% Lab Quizzes
~30% Final
Note: These percentages are a guideline and may vary plus or minus 5% at the instructor’s discretion with the
caveat that the individual components must be greater than 50%.
Note: Lab Assignments and Homework may be individual or with a partner – instructor’s discretion.
Grades will be assigned on the basis of total points earned relative to the following scale:
Effective Fall 2012
90% or greater A
80% to 89% B
70% to 79% C
60% to 69% D
below 60% F
Final grades may be based upon a curved scale representing lower point values than those above.
Course policy:

Retests/makeup tests:
You are expected to take the lab exams and lab quizzes on the scheduled days. You will receive a 0 for an
unexcused absence. To receive an excused absence you must make arrangements before the lab quiz./exam and
the lab quiz/exam must be taken early (unless a documented emergency situation has occurred).

Attendance
Students are expected to attend all classes. Failure to attend will result in a reduced participation grade.

Statement on plagiarism and cheating
Graduate business students are expected to meet the highest standards of professional integrity. Student
behavior should set an example of integrity and serve to elicit trust from those that work with them.
Graduate business students are held accountable to their peers and their faculty for their actions. (MBA
Oath, adapted.) Academic dishonesty is not tolerated in any course within the graduate business program.
Academic dishonesty includes, but is not strictly limited to the following: cheating by way of using any
unapproved written or digital notes, study aides, or the work of someone other than yourself; the
falsification, fabrication, or use of misleading data, information, or citations in any assignment; plagiarism;
submitting academic work prepared for a different class without the knowledge and approval of the
instructor; unsanctioned collaboration between students or groups of students; misrepresentation of personal
circumstances to justify an extended deadline or makeup exam or assignment; forgery; violation of
copyright; taking undue personal credit for team projects and activities; assisting or knowingly allowing
another student to violate the academic dishonesty policy. If suspected, violations of the academic
dishonesty policy will be investigated by the instructor and will be reported according to the policies set
forth by the W. A. Franke College of Business and Northern Arizona University. Confirmed violations will
result at a minimum in a point deduction of twice the grade for the specific work in question and may result
in a failing grade for the course.
University policies
Attach the Safe Working and Learning Environment, Students with Disabilities, Institutional Review
Board, and Academic Integrity policies or reference them on the syllabus. See the following document for
policy statements: http://www4.nau.edu/avpaa/UCCPolicy/plcystmt.html.
Effective Fall 2012