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Insert name of course

Course Stats

Instructor(s):  Kevin Gross (fall), Jason Osbourne (spring), Pete Thompson (Spring)

Units:  3

Semester Offered:  Fall, Spring, has pre-requisite(s) 

Description

Teaching Philosophy and Overview:  A standard misconception about statistics education is that the goal of a statistics class is to introduce the specificstatistical procedures that students in the class will need in their future research.  Not only does the proliferation of specialized statistical tests make this impossible, such a recipe driven class would be remarkably boring. Instead, the goal of an applied statistics course with a diverse audience is to teach the fundamental statistical concepts and models from which more specialized methods are derived.  An understanding of these basic models empowers students to master the specialized techniques that are specific to their own research fields.

Skills and Career Applications

Insert comments on the intent and career applications of this class. Comments/Quotes from Nicholas Alumni are especially valued.

Registration Advice

Insert comments here on how prospective students should approach registration for this class (hard to get into; sign up early; permission numbers; don't take if you already have heavy load; etc.)

Not hard to get in; follow the procedure for intra-institutional registration.  Once you get the registration email, set up your NCSU PackPortal and email forwarding b/c Prof. Thompson sends out emails.

You have to have had intro statistics equivalent to the topics covered in NCSU's ST 511;  the professor will have to approve you registering for this course and he'll want to know what you've had/haven't had before he gives the okay to register.

 Watch out for different NCSU's spring break timing as compared to Duke's and UNC if you're taking classes elsewhere.

Carpooling is probably your best bet!  You can take a TTA bus to NCSU in the morning and catch one back to and from the North Parking garage of the Tobacco Campus; but if you're in the night section of the lab, then carpooling is best.

Student Opinions

This space is intended to compile general comments from past students on the course. It will be judiciously moderated for extreme and unprofessional comments, but otherwise, it is open to a broad range of student feedback.

I took this course in F'08 - and it (surprisingly) was the best course I took in the fall.  We used SAS and R - but both were explained well and instructions are given when necessary.  Kevin Gross is a great teacher - extremely thoughtful and articulate.  You do not have to attend the lab for this class, making it a 2 days/week commitment.  Don't let the drive scare you - get a car pool at take stats at NCSU!

Kevin Gross teaches SAS and R. He is a great teacher - very flexible and accomodating for Duke students. He scheduled our exam early in Fall 2009 so we wouldn't have to hang around after Duke classes ended. I felt like I got a great education with him. His notes are really helpful for going back later and doing your own analyses.

I took this course in 2008, but have found the Spring 2009 syllabus and copied and attached this here. 

I recommend Prof. Jeff Thompson, if he's still teaching this course when you read this post.  His teaching style is deliberate and he lays out topics very simply and clearly.  He's at NCSU in a teaching capacity instead of research only; so his goals (and he tells you this) are to make sure you understand the scope and limitations of the major statistical tests you learn about (regression/SLR, MLR, ANOVAs, etc)

A solid Statistic's course that uses SAS instead of STATA or R.  SAS IS available on the computers in Perkins and in the French Center basement; but once you're registered with NCSU, you can download SAS for free from their IT department once you have your NCSU student ID set up/active.

The Instructor's Take...

This space is exclusively for the instructor to broadly comment on his or her course, and respond to commonly received feedback, explain methods and approaches, and encourage student registration. Instructors: Please limit to 300 words, or link to a wiki page of your own creation to explain in detail.

Documents and Links

  • www4.stat.ncsu.edu/~thompson/st512
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    -->ST 512 001: Experimental Statistics for Biological Sciences II
    Spring 2009
    (T, H 4:30-5:45 Broughton 3218)
    Instructor:   Dr. Jeff Thompson
     
    Office:  Patterson Hall, Room 209D
     
    Phone:   515-2570
     
    E-mail:  thompson@stat.ncsu.edu(or jrthomp4@ncsu.edu)   
                 
    Personal Web Page:* *http://www4.stat.ncsu.edu/~thompson\\
    Look for additional course information on my web page throughout the semester.  It is a good idea to check this web page once or twice a week for reminders and announcements.
     
    Office Hours:   Mondays from 1:30-3:00 and Thursdays from 1:00-2:30
                             or by appointment; impromptu visits also welcome.
     
    Text:   Experimental Design and Data Analysis for Biologists, by Gerry P. Quinn and Michael J. Keough, CambridgePublishing, 2002.
               Text and a calculator should be brought to class daily.
              
    Course Objective:   Covariance, multiple regression, curvilinear regression, concepts of experimental design, factorial experiments, confounded factorials, individual degrees of freedom and split-plot experiments. Computing laboratory addressing computational issues and use of statistical software.
     
    Teaching Philosophy and Overview:  A standard misconception about statistics education is that the goal of a statistics class is to introduce the specificstatistical procedures that students in the class will need in their future research.  Not only does the proliferation of specialized statistical tests make this impossible, such a recipe driven class would be remarkably boring. Instead, the goal of an applied statistics course with a diverse audience is to teach the fundamental statistical concepts and models from which more specialized methods are derived.  An understanding of these basic models empowers students to master the specialized techniques that are specific to their own research fields.
     
    On use of computers:  In practice, nearly everyone uses statistical software to analyze data.  While software is a tool for easing the computational burden, understanding and intelligently interpreting the output of an analysis requires knowing the theory and mathematics that underpin the statistics. Consequently, students in this class are required to understand the math behind the statistics, and possibly demonstrate that knowledge by executing certain mathematical calculations on exams.
     
    Anticipated Topics to Cover (with approximate text chapters) and Tentative Schedule:
    * *Introduction and Review of Inference/Simple Linear Regression (Chaps 1-5)                         Weeks 1-2                                                            
     Multiple regressions (Chap 6)                                                                                                    Weeks 2-5
    Experimental Design (Chap 7)                                                                                                   Week 6
                        Review of Analysis of variance (Chap 8)                                                                                   Week 7
                        Contrasts and multiple comparisons (Chap 8)                                                                            Week 8-9
                        Multifactor Analysis of variance (Chap 9)                                                                                 Week 9-11
                       Blocked designs (Chap 10)                                                                                                         Weeks 12-13
    Split plot designs (Chap 11)                                                                                                         Week 14
    Analysis of covariance (Chap 12)                                                                                               Week 15
                       
    Prerequisite:  ST 511 (Basic Statistical Inference and Experimentation) or equivalent
     
    Lab Assignments:* *Approximately 10 lab assignments will be given during the semester. Lab assignments will be posted on the course website sometime on Monday of the week that a lab is assigned.  Students should be able to make substantial progress on lab assignments during their regularly scheduled lab period, however, do not assume that you will always be able to finish the lab during the scheduled period.  The TA's are not required to stay late to help students finish!  Attendance at lab periods in not required, but is strongly encouraged.  The turn in procedure for lab assignments will be to turn in a printed copy of the lab assignment (or lab report) the week immediately following the lab at the beginning of each student's lecture meeting time.  Graded labs will be returned in lab sections.  Labs may often be a combination of work to be done on computers as well as theoretical exercises to be done by hand.  Late submissions WILL NOT be accepted.  At the end of the semester, each student will have their lowest individual lab grade replaced with their highest grade. 
     
    Lab assignments are worth 10 points each.  Working together on lab assignments, as needed, is encouraged.  Each student MUST compose and write their own programs, analyses, and final write-ups.  Labs meet in the SICL computer lab, located on the ground floor of Harrelson Hall (room G100).  SAS is the software package to be used.  Knowledge of SAS is not a prerequisite, but basic knowledge of the UNITY computer network is assumed.
     
    Study Guides:  Study guides will be posted occasionally on the course website.  Study guides are provided in lieu of additional written homework and as a means of obtaining additional practice working problems.  Reviewing study guides will be an essential way to prepare for exams.  Study guides are not collected nor graded.
     
    Exams:  
      
                                          Exam 1:                Tuesday, February 17             100 points      
                                          Exam 2:                Tuesday, March 31                 100 points     
                                          Final Exam           Tuesday, May 5                      100 points     
                                          ***Final exam time 1:00-4:00 pm
     
    Exams are closed book and closed notes.  You are allowed to use one 8.5 x 11 inch piece of paper with formulas written on it and a calculator.   One cumulative make-up exam will be given on Thursday, April 16th (time by appointment) for students missing either the first or second exam given prior permission to do so. Appropriate written documentation must be provided prior to missing the scheduled, in-class exam.  This cumulative make-up will count only for the one missed exam for students given prior permission.  A picture ID should be brought to all tests.  All test dates are tentative; any changes will be announced in class.
     
    Grading: Your final course grade is broken down as follows: 
     
    Labs                 25%
    Exam 1            25%
    Exam 2            25%
    Final Exam       25%