## STAT 5302 --- Applied Statistics I --- Fall 2007

### 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: 11:00-11:50am daily, in Math 016.
Office Hours: 12:00-12:50pm Mon & Wed, 1:30-2:30 Thurs.

### Text Book

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].

### Course Objectives and Syllabus

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. Primary objectives are to:
• 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.
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.

### Grading and Other Policies

• Your course grade will be based on a mix of written Assignments (35%), Homework problems (15%), and in-class Tests (50%). Although discussion with others in broad conceptual terms is encouraged for the Assignments and Homeworks, 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!

### Homework Problems

There will be weekly problem sets from the book due on fridays. 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.

• Set 1 (due Sep 9): 1.1, 2.3, 2.11, 3.63.
• Set 2 (due Sep 14): 3.65, 3.87, 4.11.
• Set 3 (due Sep 21): 4.20, 4.33, 4.35, 4.41.
• Set 4 (due Sep 28): 4.65, 4.77, 4.80, 4.97.
• Set 5 (due Oct 19): 5.13, 5.21, 5.32, 5.33, 5.47, 5.58, 5.69.
• Set 6 (due Oct 26): 6.4, 6.16, 6.20.
• Set 7 (due Nov 2): 6.29, 6.33, 7.17.
• Set 8 (due Nov 19): 8.2, 8.6, 8.7.
• Set 9 (due Nov 30): 8.9, 8.10, 8.12, 8.13.

### Assignments

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.

### Tests

There will be 2 semester tests and a comprehensive final exam.
• Test 1 (friday Oct 5): Over Unit 1 and counts for 15% of course grade.
• Test 2 (friday Nov 9): Over Units 2 & 3 and counts for 15% of course grade.
• Final Exam (tuesday Dec 11, 5:30-7:30pm): Emphasis on Unit 4 and counts for 20% of course grade.

### 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. 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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