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ST731:
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ST731:
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Course prerequisites: ST512 Required text (MS): Richard A. Johnson and Dean W. Wichern (2002). Applied Multivariate Statistical Analysis, 5th Edition. Prentice Hall. ISBN: 0-13-092553-5 Homework: Homework will normally be assigned at the end of class each Wednesday and will be due at the beginning of class the following Monday. Homework will be posted on the homework page. Unexcused late homework will not be accepted. The TA will grade the even-numbered problems and assign a score for each homework set. The final homework average will be computed after dropping the lowest grade. It is important to check the homework page often since it is updated regularly. Examinations: Examinations will be closed book and closed notes. However students will be permitted to bring one 8½ by 11 inch sheet of notes (both sides, any content) to each of the exams. The final exam will be cumulative. Bring calculators to all exams. Exam schedule (subject to revision):
Computer Labs: You may use your Unity account to access computer located in university computer laboratories. Information is available about the SICL Computer Lab (located in G100 Harrelson and may be used when not being used by a class), PAMS Computer Labs, and Unity Computer Labs. Asking questions: If you have questions about lectures, homework assignments, exams, procedures or any other aspect of the course please log onto http://courses.ncsu.edu/ , follow the links to "ST" and "ST731" and click on "Message Board". Then click on "Post New Topic", enter your question in the Message box, and click on "Submit Message". I will return a response. Everyone in the class will be able see your question and my response. Anonymous mail: If you wish to send an anonymous suggestion or reminder to me, send email to st731-001-comments@wolfware.ncsu.edu. The system will remove mail headers, but you must remember to removes your signature or other identifying information. Grading System (subject to revision): Final grade will be based on: Final Semester Score = (HW + M1 + M2 + 2xF)/5 where HW is the homework average (out of 100) after dropping the two lowest scores and M1, M2 and F are the scores (out of 100) on the two midterm exams and the final exam. Grades will be assigned on the ± scale according this grade scale. Auditing: Auditors are expected to attend class regularly and submit homework on the same schedule as the other students. The final grade for auditors (AU or NR) will be based on their final homework average. Policy on Academic Integrity: The University policy on academic integrity is spelled out in Appendix L of the NCSU Code of Student Conduct. For a more though elaboration see the NCSU Office of Student Conduct website. For this course group work on homework is encouraged. However copying someone else's work and calling them your own is plagiarism, so the work you turn in should be your own. With homework assignments students may work together. However, each student must submit their own own work written to demonstrate their ability to complete the assignment independently. With take-home examination, all work must be carried out independently. Working together or consulting others is not permitted. Students with Disabilities: Reasonable accommodations will be made for students with verifiable disabilities. In order to take advantage of available accommodations, students must register with Disability Services for Students (DSS), 1900 Student Health Center, CB# 7509, 515-7653. Please note: Information provided on this webpage is subject to change. Reference material (Items on reserve at D.H. Hill indicated by "RV") Joseph F. Hair, Bill Black, Barry Babin, Rolph E. Anderson, Ronald L. Tatham (2006). Multivariate Data Analysis, 6th Edition. Prentice Hall. ISBN: 0130329290 (early edition QA278.M85 1995) Bryan F. J. Manly (2005). Multivariate Statistical Methods: A Primer, 3rd Edition. Chapman & Hall/CRC. ISBN: 1584884142 (QA278.M35 2005) Donald F. Morrison (2005). Multivariate Statistical Methods, 4th Edition. Duxbury Press. ISBN: 0534387780 (QA278.M68 2005) RV T.W. Anderson (2003). An Introduction to Multivariate Statistical Analysis, 3rd edition. Wiley-Interscience. ISBN 0471360910 (QA278.A516 2003) RV Alvin C. Rencher (2002). Methods of Multivariate Analysis, 2nd Edition. John Wiley & Sons ISBN: 0471418897 (QA278.R45 2002) James P. Stevens (2002). Applied Multivariate Statistics for the Social Sciences, 4th Edition. Lawrence Erlbaum Associates. ISBN: 0805837760 (QA278.S74 2002, CD available) RV Brian S. Everitt and Graham Dunn (2001). Applied Multivariate Data Analysis, 2nd Edition. Oxford University Press. ISBN: 0340741228 (QA278.E88 2001) Brian S. Everitt, Sabine Landau and Morven Leese (2001). Cluster analysis, 4nd edition. Arnold Publication. ISBN: 0340761199 (3rd edition QA278.E9 1993) Barbara Tabachnick and Linda Fidell (2001). Using Multivariate Statistics, 4th Edition. Allyn and Bacon. ISBN: 0321056779 (QA278.T3 2001) RV Ravindra Khattree and Sagar Naik (1999). Applied Multivariate Statistics with SAS Software, 2nd Edition. SAS Press. ISBN: 0471322997 (QA278.K47 1999) Dallas E. Johnson (1998). Applied multivariate methods for data analysis. Duxbury Press. ISBN: 0534237967 (QA278.J615 1998 CD available) RV Laurance G. Grimm and Paul R. Yarnold (1995). Reading and understanding multivariate statistics. Washington, DC: American Psychological Association. (QA278.R43 1995) J.D. Jobson (1994). Applied Multivariate Data Analysis : Volume II: Categorical and Multivariate Methods. Springer-Verlag. ISBN: 0387978046 (QA278.J58 1991 v.2) K.V. Mardia, John T. Kent and John M. Bibby (1979). Multivariate Analysis. Academic Press. ISBN: 0124712525 (QA278.M36) D.N. Lawley and Albert Earnest Maxwell (1971). Factor analysis as a statistical method. American Elsevier Pub. Co. (Classic on factor analysis) (QA278.5.L38 1971) RV
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Course objectives: Multivariate statistical methods are applied to data for which two or more variables are measured for each observation. Some multivariate methods are generalizations of common "normal theory" univariate statistical procedures such as t-tests, analysis of variance and multiple regression. Others analyse the variance-covariance structure among the several variates. These may lead to reduction in dimensionality or creation of canonical variables. Classification procedures discover how groups of observations form natural clusters, while discrimination procedures give rules for classifying new observation in one of several established groups. Scaling methods develop a multidimensional mapof a set of entities so that the distances between pairs of entities match a set of dissimilarities relating the pairs. The objective of course ST731 Applied multivariate Analysis is to present regression analysis as a data analytic tool. Students will learn to develop models, fit them using SAS, assess the quality of the fit and draw conclusions based on the results of statistical analyses of the data. The course will present regression methodology as a logical extention of more standard methods such t-testing and ANOVA. Students will become acquainted with a variety of standard regression models, including important special cases such as polynomial models in one or more independent variables, models with one or more qualitative independent variables, and segmented models. Strategies for model building, variable selection, and variable transformation will be presented. Issues relating to lack of fit of the model, violation of assumptions, and computational problems will be discussed along with methods for diagnosing and remedying such problems. Time permitting, topics in logistic regression, nonlinear regression and forecasting will be discussed. Students will gain considerable experience working with data. Data from examples and problems in the text are provided on a CD. Students will use SAS to do projects and most homework assignments. Students taking the course will have completed two semesters of statistical methodology (ST511-2) and have taken a course in matrix or linear algebra (like MA 405). Syllabus (tentative): Review of univariate statistical methods Review of linear algebra Multivariate normal distribution Hotelling T2 - Inference regarding one or two mean vectors Simultaneous inference Multivariate Analysis of Variance Principal Components Analysis Factor Analysis Canonical Correlations and variables Classification Cluster Analysis Multidimensional Scaling |