STAT 5303 --- Applied Statistics II --- Spring 2012

Basic Information

Course instructor: Dr. Alex Trindade, 228 Mathematics & Statistics Building.
E-mail: alex.trindade "at"; 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.

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: 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: 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


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.

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.