Course Title: Statistical Computing
Quarter/Year: Autumn 2012
Course Number, Section and CRN: STAT 3350/4350 Sect. 1, CRN 2315 / Sect. 1, CRN 2316
Prerequisites: STAT 1400 – Statistics II or STAT 4610 – Quantitative Methods
Meeting Place and Time: DCB 230 2:00pm-3:50pm
Name of Professor: Dr. Kellie Keeling
Office Hours: TT 12:00-2:00, 4:00-4:30, Virtual Office Hours as Posted, and by Appointment
Discussion Board: The General Questions area should be used to ask questions that may be relevant to all
the students in the course. The instructor will log on to the discussion board nearly every day.
Office Location: DCB 590
E-Mail Address: SC@statsdairy.com
Phone Number: 303-871-2296
Class Web Presence: http://statsdairy.com and http://blackboard.du.edu/
ALL STUDENTS NEED TO FOLLOW THESE EXPECTATIONS:
University of Denver Honor Code
All students are expected to abide by the University of Denver Honor Code. These expectations include the
application of academic integrity and honesty in your class participation and assignments. The Honor Code
can be viewed in its entirety at this link: http://www.du.edu/ccs/honorcode.html
All members of the University of Denver are expected to uphold the values of Integrity, Respect, and
Responsibility. These values embody the standards of conduct for students, faculty, staff, and administrators
as members of the University community.
In order to foster an environment of ethical conduct in the University community, all community members
are expected to take “constructive action,” that is, any effort to discuss or report any behavior contrary to
the Honor Code with a neutral party. Failure to do so constitutes a violation of the DU Honor Code.
Specifically, plagiarism and cheating constitute academic misconduct and can result in both a grade penalty
imposed by the instructor and disciplinary action including suspension or expulsion. As part of their
responsibility to uphold the Honor Code, instructors reserve the right to have papers submitted through
SafeAssign to check for plagiarism against a database of papers submitted previously at DU, a national
database of papers, and the Internet.
Official Communications
The standard method of communicating official information from the Daniels College of Business to its
students is through email. Students are provided a DU account using the protocol of
firstname.lastname@du.edu, but must set up a "preferred" off-campus email address. Emails sent to the DU
account will be forwarded to the preferred email account. DU accounts do not store messages. More
information is available at: http://www.du.edu/studentemail/.
Students with Disabilities
A student who qualifies for academic accommodations because of a disability must submit a Faculty Letter
to the instructor from the DU Disability Services Program (DSP) in a timely manner, so that the needs of the
student can be addressed. Accommodations will not be provided retroactively, e.g., following an exam or
after the due date of a project. DSP determines eligibility for accommodations based on documented
disabilities. DSP is located in Ruffatto Hall, 1999 E. Evans Ave. (303-871-2278).
http://www.du.edu/studentlife/disability/dsp/index.html
Performance Assessment
The Daniels College of Business may use assessment tools in this course and other courses for evaluation.
Educational Assessment is defined as the systematic collection, interpretation, and use of information about
student characteristics, educational environments, learning outcomes and client satisfaction to improve
program effectiveness, student performance and professional success.
Gifts from Students
Because of possible perceptions of undue influence, it is not appropriate for a student to give a gift to a
faculty member while the student is still enrolled in that faculty member’s class, including through the
grading period. As a general rule, Daniels discourages the giving of gifts between students and faculty.
Emergency Procedures
The College places great emphasis on the safety of its students. Please respect emergency instructions,
including fire alarms. For more information, go to
http://www.du.edu/emergency/whattodowhen/index.html
REQUIRED COURSE MATERIALS:
Course Description
This course will provide the student with a base of skills necessary to program in the SAS and R software
packages. No prior programming knowledge is required. After completion of the course the student will be
able to independently perform most basic statistical procedures using either software package. The student
will also have the tools necessary to learn advanced topics from SAS and R documentation by themselves.
Beyond Grey Pinstripes: In this course we will discuss issues around ethics in statistical analysis as related to
Business and Society. Specific concerns are misleading statistics and graphs and personally identifiable
information that could lead to Discrimination, Privacy abuse, and Telemarketing abuses.
Required Materials
• Clicker response system device – These are available new & used from the bookstore->
• Software
SAS On Demand (instructions for installing on Blackboard)
R and R Studio (instructions for installing will be on Blackboard halfway through
course)
Camtasia Relay - I will send you information about downloading
Course Assessment
Performance will be evaluated on the items below. For this class, all assignments and exams assume you
are trainees for Stats Dairy. Your training score is only a measure of your performance in this class and does
not reflect my opinion of you as an individual or your worth as a person.
Module Exam 1
10%
Module Exam 2
15%
Module Exam 3
15%
Module Exam 4
15%
Mini Assignments
10%
Statistical Briefs
25%
Project
10%
100%
Grades: Stats Dairy regularly hires more data mining trainees than it needs. By means of this course we
determine where to place the graduates of the program:
90% - 100%
A
Trainees who receive an A are considered on the "fast track" and will start out as
data mining analysts. Our studies show that most trainees who fall in this group
reach an executive position within 10 years.
80% - 89%
B
Trainees who receive a B will start out as assistant data mining analysts. This does
not mean that they cannot reach the executive level but it will be more difficult
since they will not regularly be put into career-enhancing positions such as overseas
consulting assignments.
70% - 79%
C
Trainees who receive a C will be put into staff positions for further development.
60% - 69%
D
Trainees who receive a D will be offered non-management positions.
00% - 59%
F
Trainees who receive an F will be separated from Stats Dairy.
NOTE: + and – grades are given according to the DU scale
Course Assignments
Exams: Exams will be completed in two parts. The first part will be completed during class time and a second "take
home" portion will also be distributed after the exam for Modules 1-3 and before the exam for Module 4. If you are
going to miss an exam for a legitimate conflict, you must receive permission from me BEFORE the exam in order to
reschedule. Otherwise you will receive a zero on the exam. No make-up exams will be given. For the portion of the
exam taken on your personal computer, you will be required to record what you are doing using Camtasia Relay.
Statistical Briefs: These are essentially small homework assignments completed individually. There will be 2 briefs
for each Module. If you score at least 50% on all 8 briefs, then your final Briefs grade will be calculated by dropping
your 2 lowest briefs. If you score at least 50% on 7 briefs, then your final Briefs grade will be calculated by dropping
your 1 lowest brief. If you do not meet either of the previous, then all 8 Briefs will count in your Briefs grade.
Mini Assignments: During class time an assignment will be given that supplements the topics learned for the day.
These can be completed in small groups or individually. These are due at the end of class or at the beginning of the
following class. The lowest three scores of these mini assignments will be dropped. In addition, participation in
clicker quizzes will be a part of this grade.
Project: There will be an individual project assigned during the course. This will involve collection and analysis of a
dataset of your choice. Deliverables for this project will be due throughout the course concluding with a Poster
presentation and final report at the end of the quarter.
Communication
If you are having difficulty with the course material, please see me at your earliest convenience. Do not wait until the
first exam to see me about difficulties you are experiencing in comprehending the course material. Do not allow
yourself to fall behind in covering the assigned material as this will most certainly result in a poor course grade. Keep
up with your assignments as we move through the course.
Honor Code
You are expected to abide by the University's honor code on all assignments and exams. The Honor Code is meant to
foster and advance an environment of ethical conduct in our academic community. The Code of Student Conduct
contains information on the behavioral standards expected of all students at the University of Denver including the
areas of civility, community, integrity, and responsibility. Details can be found at: http://www.du.edu/ccs/
Classroom Environment
The optimal learning environment may be impaired significantly when the class as a whole is distracted from its
intended focus by the actions of a few. Accordingly, classroom computers should be used only as directed by the
instructor. Also, in-class use of cell phones, beepers and other devices that potentially may create classroom
distractions is prohibited (e.g., cell phones must be set on silent). Further, the behavior of each member in the class
must be conducive to the learning of the class as individuals and as a whole.
Students with Special Needs
If you need adaptations or accommodations because of a disability and are registered with the Disability Services
Program (DSP), if you have emergency medical information to share with me, if you need special arrangements in
case the building must be evacuated, or if you require rescheduling of an exam due to a religious holiday, please
make an appointment to see me during the first week of class.
Course Schedule
This is a preliminary outline for the course. Depending on the desires of the students and our exploration of the
software as the quarter progresses, the exact topics may change but the overall structure (exam dates, project due
date) will stay as posted.
Individual details will be kept updated here: http://statsdairy.com/statcomp/indexStatComp.html
Date
Principal content elements
Tue Sep 11
Getting Started with SAS/Reading SAS Datasets
Thu Sep 13
Reading Other Datasets/SAS Syntax
Tue Sep 18
Statistical Analyses with SAS
Thu Sep 20
Validating, Cleaning, Manipulating Data/Combining SAS Datasets
Tue Sep 25
Module 1 Exam (take home post)
Thu Sep 27
Enhancing and Producing Summary Reports
Tue Oct 2
Graphing with SAS/Graph
Thu Oct 4
Summarizing Data/Using Queries with SQL
Tue Oct 9
SAS Wrap-up
Thu Oct 11
Module 2 Exam (take home post)
Tue Oct 16
Getting Started with R/R Studio
Thu Oct 18
Basic Capabilities in R
Tue Oct 23
Statistical Analysis with R / Ethics in Statistical Analysis
Thu Oct 25
Data Manipulation in R
Tue Oct 30
Module 3 Exam (take home post)
Thu Nov 1
Graphics in R
Tue Nov 6
Advanced Graphing in R
Thu Nov 8
Advanced Data Manipulation
Tue Nov 13
R Wrap-up
Thu Nov 15
Project Poster Presentations
Tue Nov 20
Module 4 Exam (take home pre)