## 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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