STAT 5302 -- Applied Statistics I -- Fall 2009
Trindade, 211 Mathematics & Statistics Building.
E-mail: alex.trindade"at"ttu.edu; Phone: 742-2580 x 233.
Course Meets: MWF 11:00 - 11:50 in MATH 108.
Office Hours: MWF 12:00 - 12:50, or by appointment.
- Required: 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]. (The 5th
edition is also acceptable.)
- Recommended: accompanying Student Solutions Manual containing
solutions to selected exercises [ISBN-13: 978-0-495-10915-0].
- Good background reads:
- The Lady Tasting Tea: How Statistics Revolutionized Science in the
Twentieth Century, (2002), by David Salsburg, Owl Books. (For the educated layman;
relays statistical developments of the 20th Century through descriptions of the famous statisticians and the problems they studied.)
- Dicing with Death: Chance, Risk and Health, (2003), by Stephen Senn, Cambridge University Press. (For the educated layman;
relays history of important statistical developments from a biostatistics perspective.)
- Statistical Models: Theory and Practice, (2005), by David
A. Freedman, Cambridge University Press. (Undergraduate/graduate level statistics, but
generally very accessible;
relays important statistical concepts through a variety of published studies
in different disciplines.)
- Short (and humorous) overview
of statistics. (Extracted from JMP Start Statistics, 2005, by
Creighton & Lehmann.)
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. 5302 will cover the first 9 chapters of the book; 5303 will attempt
to cover the remainder. Syllabus for 5302:
- Basic statistical concepts; numerical and graphical data summaries.
- Basic concepts relating to probability; random variables; sampling
distributions; the Central Limit Theorem.
- Inference about one population central value (mean, median): one-sample z-test;
t-test; sign test.
- Inference about two population central values (means, medians): two-sample
z-test; t-test; paired t-test; Wilcoxon rank sum and signed rank tests.
- Inference about population variances: one, two, and more than two samples.
- Inferences about more than two population central values (means, medians):
analysis of variance (ANOVA); Kruskal-Wallis test.
- Multiple comparison procedures in ANOVA.
Expected Learning Outcomes
After completing the STAT 5302-5303 sequence the student should be able to:
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.)
- 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.
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.
Methods of Assessing the Expected Learning Outcomes
- 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!
There will be weekly problem sets from the book due (mostly) 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.
Homework solutions and grades will be posted on your eLearning tab, accessible through raiderlink.
- Set 1 (due Sep 4): 1.1, 2.13, 2.15(a), 2.23. (PDF.)
- Set 2 (due Sep 11): 3.32, 3.33, 3.40.(PDF.)
- Set 3 (due Sep 18): 4.11, 4.19, 4.23, 4.41, 4.45. (PDF.)
- Set 4 (due Sep 25): 4.61, 4.69, 4.73, 4.77, 4.83. (PDF.)
- Set 5 (due Oct 14): 5.10, 5.15, 5.18, 5.29, 5.34. (PDF.)
- Set 6 (due Oct 23): 5.41, 5.42, 5.59, 5.60. (PDF.)
- Set 7 (due Nov 2): 6.13, 6.18, 6.23, 6.31, 6.37, 7.15, 7.18. (PDF.)
- Set 8 (due Nov 13): 8.7, 8.8.
- Set 9 (due Nov 23): 8.15, 8.19, 8.29, 8.30. (PDF for Sets 8 & 9.)
- Set 10 (due Dec 7): 9.3, 9.13-18. (PDF)
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 (room 201).
There will be 2 semester tests and a comprehensive final exam.
- Test 1 (15%): Friday, Oct 2. (Unit 1)
- Test 2 (15%): Friday, Nov 6. (Units 2 & 3)
- Final Exam (20%): Friday, December 11, 1:30 - 4:00 p.m. (Instructions)
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, 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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