E-mail: alex.trindade "at" ttu.edu; 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.

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 take home 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.

- Assignment 5: Due Thursday Feb 9.
- Assignment 6: Due Thursday Feb 23.
- Assignment 7: Due Thursday March 8. Solution.
- Assignment 8: Due Thursday March 29.
- Assignment 9: Due Thursday April 12.
- Assignment 10: Due Thursday April 26.

- Midterm Test: Thursday March 8.
- Final Exam: Due 17:00 Friday May 11.

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.

**Class Attendance**. Your attendance alone will not impact your grade, but missing exams and assignments will. Whether an absence is excused or unexcused is determined solely by me, with the exception of absences due to religious observance and officially approved trips (see below).__Make-up Exams__: These may be granted in exceptional circumstances if you provide me with a valid excuse (such as a note from a physician, an obituary, etc.).__Absence due to religious observance__: The Texas Tech University Catalog states that a student shall be excused from attending classes or other required activities, including examinations, for the observance of a religious holy day, including travel for that purpose. A student who intends to observe a religious holy day should make that intention known in writing to the instructor prior to the absence. A student who is absent from classes for the observance of a religious holy day shall be allowed to take an examination or complete an assignment scheduled for that day within a reasonable time after the absence.__Absence due to officially approved trips__: The Texas Tech University Catalog states that the department chairpersons, directors, or others responsible for a student representing the university on officially approved trips should notify the student's instructors of the departure and return schedules in advance of the trip. The instructor so notified must not penalize the student, although the student is responsible for material missed. Students absent because of university business must be given the same privileges as other students.

**Illness and Death Notification**. The Center for Campus Life is responsible for notifying the campus community of student illnesses, immediate family deaths and/or student death. Generally, in cases of student illness or immediate family deaths, the notification to the appropriate campus community members occur when a student is absent from class for four (4) consecutive days with appropriate verification. It is always the student's responsibility for missed class assignments and/or course work during their absence. The student is encouraged to contact the faculty member immediately regarding the absences and to provide verification afterwards. The notification from the Center for Campus Life does not excuse a student from class, assignments, and/or any other course requirements. The notification is provided as a courtesy.**Students with Disabilities**. Any student who because of a disability may require special arrangements in order to meet course requirements should contact the instructor as soon as possible to make any necessary accommodations. Student should present appropriate verification from AccessTECH. No requirement exists that accommodations be made prior to completion of this approved university procedure.**Civility in the Classroom**. It is expected that everyone will behave in a manner that is conducive to learning. One common disruption is cell phones. Please turn these off in class.**Academic Integrity**. Is assumed and expected at all times. Students are advised to acquaint themselves with the Code of Student Conduct.*It is the aim of the faculty of Texas Tech University to foster a spirit of complete honesty and a high standard of integrity. The attempt of students to present as their own any work that they have not honestly performed is regarded by the faculty and administration as a serious offense and renders the offenders liable to serious consequences, possibly suspension.*

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