STAT 5303 --- Applied Statistics II --- Spring 2010
Basic Information
Course instructor:
Dr. Alex
Trindade, 211 Mathematics & Statistics Building.
E-mail: alex.trindade "at" ttu.edu; Phone: 742-2580 x 233.
Course Meets: 10:00-10:50am MWF, in Math 010.
Office Hours: 11:00-12:00 MWF.
Text Book
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:
- 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.
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.
Expected Learning Outcomes
After completing the STAT 5302-5303 sequence the student should be able to:
- 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.
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.
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 comprehensive 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. Once final grades have
been awarded there will be NO APPEALS!
Assignments
There will be approximately 5 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 by noon, either to me in class or to the secretary in the main office.
Tests
There will be a midterm test and a comprehensive final exam.
- Midterm Test (before spring break).
- Final Exam (Tue May 11, 7:30-10:00 am).
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 (MAM052),
which can be found in room 238 has Minitab (PC running windows closest to the
fridge), and it is connected to a printer in the same room.
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