E-mail: alex.trindade "at" ttu.edu; Phone: 742-2580 x 233.

Course Meets: 10:00-10:50am MWF, in Math 016.

Office Hours: 11:00-12:00 MWF.

Required: *An Introduction to Statistical Methods and Data Analysis*, 5th edition (2001), R. Lyman Ott and Michael Longnecker, Duxbury-Thomson-Brooks/Cole, Belmont, CA [ISBN: 0-534-25122-6].

Also strongly recommended is the accompanying Student Solutions Manual containing solutions to selected exercises [ISBN: 0-534-37123-X].

- Provide a foundation in basic statistical concepts, numerical and graphical data summaries.
- Introduce statistical inference based on the t, F and Chi Square tests.
- Introduce simple linear and multiple regression analysis.
- Introduce statistical aspects of experimental design and the associated analysis of variance (ANOVA) and analysis of covariance (ANCOVA).
- Introduce statistical aspects of categorical data analysis.
- Introduce more advanced concepts and methods in: design of experiments; mixed models; factorial experiments; repeated measures designs.

- 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.
- Course averages of at least 90% and 80% will guarantee the
passing grades of A and B, respectively. Course averages below 80% are
candidates for the failing 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!**

- Assignment 5: Mon Jan 28.
- Assignment 6: Wed Feb 13.
- Assignment 7: Wed Feb 27.
- Assignment 8: Mon Mar 10.
- Assignment 9: Wed Apr 9.

- Midterm Test (Fri Mar 14): Over Units 5-8.
- Final Exam (Tue May 6, 1:30-4:00pm).

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**. 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 become acquainted with **R**; but be aware that it is a steep learning curve! Follow this
link to access some of my resources on statistical computing, particularly R.

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