STAT 5303 --- Applied Statistics II --- Spring 2012
Trindade, 228 Mathematics & Statistics Building.
E-mail: alex.trindade "at" ttu.edu; Phone: 742-2580 x 233.
Course Meets: TR 14:00 in Math 112
Office Hours: Tue 11:00 - 12:00, Wed 12:00 - 13:00, or by appointment.
Strongly Recommended: An Introduction to Statistical Methods and Data Analysis, 6th edition (2010), R. Lyman Ott and Michael Longnecker, Duxbury-Thomson-Brooks/Cole, Belmont, CA [ISBN-13: 978-0-495-01758-5].
Recommended: accompanying Student Solutions Manual containing solutions to selected exercises [ISBN-13: 978-0-495-10915-0].
Syllabus and Lecture Notes
STAT 5302-5303 is an introductory sequence of courses for graduate students in the life, biological, agricultural, or social sciences, who have little or no background in statistics, yet who plan to use statistical techniques in their
research. The emphasis is on the analysis of data. Familiarity with the
material covered in STAT 5302 is assumed in STAT 5303. STAT 5302 covers the first 9 chapters of the book; 5303 will attempt
to cover the remainder. Syllabus for 5303:
Lecture notes are available here in ppt format for some Units. Topics without
pre-prepared slides will likely be covered on the board. You'll find it helpful to augment the lecture notes by reading the appropriate sections in the book.
- Introduce more advanced concepts and methods in design of experiments:
blocking; randomized block designs; latin square designs; factorial experiments; repeated measures.
- Analysis of variance (ANOVA) for two and more factors.
- Introduce simple linear and multiple regression analysis.
- Analysis of covariance (ANCOVA).
- Introduce aspects of categorical data analysis: chi-square tests of
independence; logistic regression; generalized linear models.
- Introduce more advanced statistical modeling concepts and methods: mixed
models; nested and crossed factors; split-plot designs.
Expected Learning Outcomes
After completing the STAT 5302-5303 sequence the student should be able to:
Secondary objectives: provide practice in using a statistical
computing package to perform the basic analyses covered; introduce a
few of the many "nonparametric" alternatives to the standard
"parametric" methods covered; discuss research data management to
facilitate data analysis.
Note that this sequence is not for mathematics, statistics, engineering, or physical science majors; these students should take STAT 5384-5385.
- Understand basic statistical concepts; construct numerical and graphical data summaries.
- Understand and perform statistical inference based on the t, F and Chi Square tests.
- Understand and perform simple linear and multiple regression analysis.
- Understand statistical aspects of experimental design and the
associated analysis of variance (ANOVA) and analysis of covariance (ANCOVA); be
able to carry out analyses of such.
- Understand statistical aspects of categorical data analysis; be
able to carry out analyses of such.
- Understand more advanced concepts and methods in: design of experiments;
mixed models; factorial experiments; repeated measures designs. Be
able to carry out analyses of such.
Methods of Assessing the Expected Learning Outcomes
- Your course grade will be based on a mix of: written Assignments (70%), a
Midterm Test (10%), and a take home Final Exam (20%).
- Although discussion with others in broad conceptual terms is encouraged for the Assignments, your
submitted work must be your own. For example: discussing how to go about
building a regression model, software commands, and even comparing final
models, is OK. But borrowing somebody's completed assignment in
order to see how they did it (meaning that you are simply going to copy what
they did without critical thinking of your own), is not.
- Course averages of at least 90% and 80% will guarantee the
grades of A and B, respectively. Course averages below 80% are
candidates for grades of C, D, and F. If your course
average starts to fall in an undesirable (or catastrophic) category,
it is your responsibility to counsel with me about what your options are, and
what you might realistically be able to get.
There will be 6 Assignments, each involving an extensive
statistical analysis of a dataset, and subsequent writing up of the results. Your
analysis should be typeset in the form of a report. More detailed instructions
will be given with each Assignment. The numbering scheme parallels that of the
Units in the Lecture List. Assignments and their due dates are as follows:
Submit a stapled hard copy either to me in class, or to the secretary in the main office (201) who
will place it in my mailbox, by 5pm. No late work will be accepted! (Extensions may be granted on a case-by-case basis if circumstances warrant it.)
There will be a midterm test and a take home final exam.
- Midterm Test: Thursday March 8.
- Final Exam: Due 17:00 Friday May 11.
Statistical Computing, Software, and Other Course Resources
Some of the more commonly used datasets in class examples, and tests, are available here.
This course will require extensive statistical computing work, most of
which you will have to pick up on your own. Since the software package you choose to do this with is up to you, the following statistical software overview may be useful. The course text shows output from MINITAB, SAS, JMP, STATA, and SPSS. The course notes have some example code and output from MINITAB, R, SAS, and SPSS.
You should probably select a software package that you already have access to
(through your home department's computing system for example) and/or will most
likely end up doing research with. Failing this, if you have no prior
programming experience I suggest you use MINITAB, or SPSS. If you are
more ambitious, computer-savvy, plan on doing extensive exploratory data analysis in
the future and preparing statistical plots for publication, I would
encourage you to learn SAS or R (both have steep learning curves). Follow this
link to access some of my resources on statistical computing, particularly R.
The public PCs in the MATH building all have SAS and R. Only one PC,
which can be found in room 238 (PC running windows closest to the
fridge), has Minitab and is connected to a printer in the same room.
- Class Attendance. Your attendance alone will not impact your grade,
but missing exams and assignments will. Whether an absence is excused or
unexcused is determined solely by me, with the exception of absences due to
religious observance and officially approved trips (see below).
- Make-up Exams: These may be granted in exceptional circumstances
if you provide me with a valid excuse (such as a note from a physician, an
- Absence due to religious observance: The Texas Tech University Catalog states that a student shall be excused from attending classes or other required activities, including examinations, for the observance of a religious holy day, including travel for that purpose. A student who intends to observe a religious holy day should make that intention known in writing to the instructor prior to the absence. A student who is absent from classes for the observance of a religious holy day shall be allowed to take an examination or complete an assignment scheduled for that day within a reasonable time after the absence.
- Absence due to officially approved trips: The Texas Tech University Catalog states that the department chairpersons, directors, or others responsible for a student representing the university on officially approved trips should notify the student's instructors of the departure and return schedules in advance of the trip. The instructor so notified must not penalize the student, although the student is responsible for material missed. Students absent because of university business must be given the same privileges as other students.
- Illness and Death Notification. The Center for Campus Life is responsible for notifying the campus community of student illnesses, immediate family deaths and/or student death. Generally, in cases of student illness or immediate family deaths, the notification to the appropriate campus community members occur when a student is absent from class for four (4) consecutive days with appropriate verification. It is always the student's responsibility for missed class assignments and/or course work during their absence. The student is encouraged to contact the faculty member immediately regarding the absences and to provide verification afterwards. The notification from the Center for Campus Life does not excuse a student from class, assignments, and/or any other course requirements. The notification is provided as a courtesy.
- Students with Disabilities. Any student who because of a disability may require special arrangements in order to meet course requirements should contact the instructor as soon as possible to make any necessary accommodations. Student should present appropriate verification from AccessTECH. No requirement exists that accommodations be made prior to completion of this approved university procedure.
- Civility in the Classroom. It is expected that everyone will behave
in a manner that is conducive to learning. One common disruption is cell
phones. Please turn these off in class.
- Academic Integrity. Is assumed and expected at all times. Students are
advised to acquaint themselves with the Code of Student Conduct.
It is the aim of the faculty of Texas Tech University to foster a spirit of complete honesty and a high standard of integrity. The attempt of students to present as their own any work that they have not honestly performed is regarded by the faculty and administration as a serious offense and renders the offenders liable to serious consequences, possibly suspension.
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