STAT 5302 -- Applied Statistics I -- Fall 2011

Basic Information

Course instructor: Dr. Alex Trindade, 228 Mathematics & Statistics Building.
E-mail: alex.trindade"at"; Phone: 742-2580 x 233.
Course Meets: 2:00 - 3:20 pm TR in Math 115.
Office Hours: Tue 4:00 - 5:00 pm, Wed 12:00 - 1:00 pm and 2:00 - 3:00 pm.



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. No prerequisite is assumed. 5302 will cover the first 9 chapters of the book; 5303 will attempt to cover the remainder. Syllabus for 5302:

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.)

Lecture Notes

Are available here in ppt format. They may be modified slightly just before and after each class. You'll find it helpful to augment the lecture notes by reading the appropriate sections in the book.

Methods of Assessing the Expected Learning Outcomes

Homework Problems

There will be weekly problem sets from the book. In addition the You are strongly advised to do (at least) all the Supplementary Exercises at the end of each book chapter. Assignment and Test questions will be similar. Start each problem on a separate page and staple everything. (Only a subset of questions will be graded.)

Homework solutions and grades will be posted on your eLearning tab, accessible through raiderlink.


There will be 3 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. Submit a stapled hard copy by noon, either to me in class or to the secretary in the main office (room 201).


There will be 2 semester tests and a comprehensive final exam. Tentative dates are:

Statistical Computing, Software, and Other Course Resources

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 (MAM437), 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.