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.

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].

- 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.

- 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.

- 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!**

- Assignment 5: Due Friday Feb 5.
- Assignment 6: Due Friday Feb 19. Solution:
- Assignment 7: Due Friday March 5. Solution.
- Assignment 8: Due Friday March 26.
- Assignment 9: Due Friday April 9.
- Assignment 10: Due Friday April 23.

- Midterm Test (before spring break).
- Final Exam (Tue May 11, 7:30-10:00 am).

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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