STAT 5303-5385 --- Applied Statistics II --- Spring 2024
Basic Information
Course instructor:
Dr. Alex
Trindade, 233 Mathematics & Statistics Building.
E-mail: alex.trindade "at" ttu.edu.
Course Meets: TR 11:00 in Holden Hall 00005.
Office Hours: Tue, Wed, Thu 1:00 - 2:00, or by appointment.
Text Book
Strongly Recommended: An Introduction to Statistical Methods and Data Analysis, 6th ed. (2010) or 7th ed. (2016), R. Lyman Ott and Michael Longnecker, Duxbury-Thomson-Brooks/Cole.
Recommended: accompanying Student Solutions Manual containing solutions to selected exercises.
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 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.
Assignments
There will be 6 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 (see General Assignment Instructions below). More specific instructions may be given with each Assignment. The numbering scheme parallels that of the Units in the Lecture List. Assignments will be posted on Blackboard. The due dates are as follows:
No late work will be accepted! (Extensions may be granted on a case-by-case basis if circumstances warrant it.)
Tests
There will be a midterm test and a take home final exam.
- Midterm Test: Thursday February 29. (sample).
- Final Exam: TBA.
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 R (Lab Room 113). TTU has software licences for the following commercial statistical packages: JMP, SPSS, SAS (TTU Software/Site License).
General Assignment Instructions
- Typeset your results as a report, using this template.
Give a brief INTRODUCTION.
In the STATISTICAL METHODS section, describe all statistical
computations (including sample size calculations) and analyses performed. Describe your
findings in the RESULTS AND CONCLUSIONS section.
Place all numeric findings into appropriate tables and refer to them in
the RESULTS AND CONCLUSIONS section. Place all graphical material
produced as a result of this analysis also into figures and refer to them
in the RESULTS AND CONCLUSIONS section. Include in this section
also your conclusion regarding the analyses (i.e. interpret the statistical
tests).
- Use complete sentences and well-structured paragraphs.
- Do not include findings in the METHODS section. Past tense is ok here.
( eg. "An F-test for equality of variances was run to ...")
- Include all findings, e.g. Tables, Figures and statistical test conclusions
only in the RESULTS AND CONCLUSIONS write-up. Try to write this section
in the present tense ("Results are ..." rather than "Results
were ...." )
- Try to minimize the inclusion of raw computer output in your report.
- Each Assignment will take some time. Don't put it off too
late. Use the
computer whenever possible to reduce computation time. You
should be spending
more time interpreting results than doing computations.
Policies
- Required Texas Tech Policies can be found here.
- Recommended Texas Tech Policies can be found here.
- Use of Generative AI Tools. The use of generative AI tools (such as ChatGPT) is not permitted in this course; therefore, any use of AI tools for work in this class may be
considered a violation of Texas Tech's Academic Integrity policy and
the Student Code of Conduct since the work is not your own. The use of
unauthorized AI tools will result in referral to the Office of Student
Conduct.
- Electronic Devices in Tests. In the spirit of keeping costs down, I will permit the usage of apps on smart devices (phones, tablets, laptops, etc.), but any kind of communication or accessing of the web via these devices is forbidden.
- Collaboration. My policies on this are as follows.
- Homeworks: Discussion with peers regarding material/concepts covered in the
course is permitted, and is encouraged since it usually leads to greater comprehension. However, each person must write up his/her own
solution to a particular problem, and not simply have someone else do it for them.
- Tests: Any form of collaboration on tests, including e-device communication or trying to see what the person next to you is writing, is strictly forbidden and will not be tolerated.
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