Course: STP 429, Experimental Statistics, 10:30-11:45 Tuesday and Thursday, ED 216

Instructor: Dr. Sharon Lohr, PS A 444,  965-4440, sharon.lohr@asu.edu    Home page: http://stat.asu.edu/~lohr

Office hours:  12:00-1:00 Tues, 2:00-3:00 Wed, 9:00-10:00 Thurs and by appointment. You may also ask questions at any time through e-mail.

Prerequisite:  STP 420 or equivalent

Text:  Faraway, J. J. (2005). Linear Models with R. Boca Raton, FL: CRC Press. The data sets used in the book are available at www.maths.bath.ac.uk/~jjf23/LMR

Grading:

    Midterm Examination                                         25%
    Final Exam (Tuesday, May 12, 9:50-11:40):       25%
    Homework, projects and class participation:       50%

Assignments Assignments and projects will be due at the beginning of class. Late homework is not accepted, but your lowest homework score will be dropped in computing your final grade. For all data analyses, you must include your R code and the relevant output. RAW AND UNINTERPRETED COMPUTER OUTPUT IS UNACCEPTABLE. Make sure you edit out unnecessary material, for example, analyses that did not work. Answer the questions about the data analysis in boldface. Your homework should be in a form that would be presentable to an employer who asked you to perform a data analysis.

Computing: We shall use the R package for statistical computing. This is free software that runs on Unix, Windows, or Mac OS. You can download the software to your laptop from www.r-project.org

ASU's Policy on Academic Dishonesty: In the “Student Academic Integrity Policy” manual, ASU defines “Plagiarism [as] using another's words, ideas, materials or work without properly acknowledging and documenting the source. Students are responsible for knowing the rules governing the use of another's work or materials and for acknowledging and documenting the source appropriately.”  You can find this definition at: http://www.asu.edu/studentaffairs/studentlife/judicial/academic_integrity.htm#definitions

Academic dishonesty, including inappropriate collaboration, will not be tolerated. There are severe sanctions for cheating, plagiarizing and any other form of dishonesty.

Policy on Incompletes: You will only be given a grade of Incomplete if all of the following statements apply to you: a) The circumstances which make it impossible for you to complete the course before the end of the semester are beyond your control and occurred within the last two weeks of the semester. b) You have been in attendance through most of the course. c) You have a passing grade on the work completed. d) You have written documentation (a doctor's excuse, for example) of your need for an Incomplete.

Final Exam Policy: I cannot reschedule the final exam for students; if you cannot take the final exam for a good reason (vacation plans or wanting to leave early are not considered "good reasons") you need to get approval from the dean of the college. See the Department of Mathematics and Statistics Final Exam Policy
 
 

Tentative Course Schedule (subject to change):
 
 

Week of	Topic	Chapter in Text
Jan 19	Review of t tests and confidence intervals; Introduction to R	Appendices
Jan 26	Straight line regression	1-2
Feb 2	ANOVA Table; Multiple regression	2
Feb 9	Multiple regression	2-3
Feb 16	Inference in regression	3
Feb 23	Regression diagnostics	4
Mar 2	Transformations	6-7
Mar 9	Spring Break!!!!!
Mar 16	Multicollinearity	5.3
Mar 23	Model selection in regression	8
Mar 30	One-way ANOVA	14
Apr 6	Multiple comparisons	14
Apr 13	Factorial designs	15
Apr 20	Randomized complete block designs	16
Apr 27	Catch-up and other topics
May 4	Review