California State University, Fresno
Department of Biology
BIOMETRY
Fall 2006
Course No: BIOL 274
Instructor: Dr. Madhusudan Katti
Unit Value: 3
Office: Room 218B, Science Building
Location: Science 211
Email: mkatti@csufresno.edu
Lectures: Th 17:00-17:50
Tel: 559•278•2460
Office Hours: M-W: 1-3:30PM
http://zimmer.csufresno.edu/~mkatti/
This syllabus and schedule are subject to change in the event of extenuating circumstances.
Introduction and Course Description
This course is intended for Biology graduate students from all disciplines within the department. Since this
department requires a thesis for an M.S. degree, you will or already have encountered issues regarding
thesis research questions and how to test those questions. This class will facilitate your development and
maturation as a scientist by providing you with material that will challenge you to think about how you will
plan, execute, and interpret your thesis research project. Also important is your exposure to fundamental
and advanced concepts in experimental design and data analysis in the biological sciences that will influence
you beyond your tenure at CSU-Fresno.
YOU WILL BE EXPECTED TO UNDERSTAND AND PERFORM BEYOND THE LEVEL OF
PREREQUISITES FOR THIS COURSE. IF YOU DO NOT UNDERSTAND, TALK TO ME OR
YOUR ADVISOR. YOU ARE RESPOSIBLE FOR THE OBLIGATIONS OF THIS COURSE AND
IT’S PREREQUISITES.
Required Text
Zar, J.H., 1999. Biostatistical Analysis, 4th Ed. Prentice Hall.
Readings: Other Textbooks
Krebs, C.J. 1999. Ecological Methodology, 2nd Ed. Addison-Wesley.
Scheiner, S.M., and J. Gurevitch. 2001. Design and Analysis of Ecological Experiments.
Quinn, G. P., and M. J. Keogh. 2002. Experimental Design and Data Analysis for Biologists.
Gotelli, N. J., and A. M. Ellison 2004. A Primer of Ecological Statistics. Sinauer.
NOTE: Of the above books, the first 3 are available in the CSU library, and I have all four in my own library
in S 218. I will hold my copies of these in reserve, and you are encouraged to come by and read them in my
conference room, or borrow for short periods if you want to photocopy sections of a book.
Readings: Other Sources
Other readings will come from book chapters and published papers as listed in the course schedule.
Primary Learning Outcomes
What you should gain from this course:
• Fundamentals in the design of experiments and what to do when controlled, replicated experiments are
not possible.
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• Graduate-level understanding of the analysis of biological data.
• Knowledge in the use of fundamental and advanced data analysis options and procedures.
• An improved understanding of experimental design, statistical analysis, and interpretation within biology
in general and your specific discipline.
Grading
Course Grade:
• Your grade is comprised of:
By %
Midterm Exams (2 @ 20% ea): 40%
Final Exam: 30%
Presentations and reports: (2 @ 10 % ea) 20%
Participation & Homework 10%
Homework includes: 3 main points or ‘new’ information from each assigned paper
• Grading scale: A=100-90%, B=89-80%, C=79-70%, D=69-60%, F=<60%
I reserve the right to adjust this scale based on the progression of assessment outcomes.
Midterm Exams:
• Midterm exams will be comprised of definitions, application of statistical tests, and problem solving.
Calculators will be allowed in exams, but only non-programmable units with single line displays.
• No make up exams except in cases of medical emergency or university commitment with documentation.
• You will be given at least one week’s notice in the event of a rescheduled exam.
Final Exam:
• ~40% material since Midterm Exam II, 60% comprehensive (expect synthesis of course material)
• The date and time is according to the Finals schedule, and in the course schedule. It is not possible to take
the final exam before this time. Plan accordingly, do not make plans for major travel that will cause
you to miss the final exam.
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Course Policies
My primary policy for this course is based on the principle of mutual respect among students and instructors. Everyone in
class has expended time and money to take this course, and deserves a quality learning environment. Thus, talking in class
(beyond whispering with your neighbor about a point made in class), active cell phones and pagers etc., use of tobacco products,
reading newspapers in class or other distracting behavior will earn the scorn of your peers. I trust it will not happen, but I reserve
the right to dismiss disruptive individuals from the lecture room.
Attendance is expected at all class periods. If you must miss a class (and there must be a compelling reason to do so) get notes
from a classmate as soon as possible. Since you are here on your own accord, attendance is not an explicit part of your grade, but
there is overwhelming evidence that poor attendance will definitely be reflected in your grade.
Important announcements will be made during class regarding exams, reading assignments, etc. and it is your responsibility to be
aware of them, as well as learning from the lectures. Also check your email as I will disseminate information to the class email list.
If you are absent from class, it is your responsibility to obtain notes from a classmate and check on announcements made while
you were away.
For university level policies see the University Policy on Disruptive Classroom Behavior in the Schedule of Courses and the
Academic Policy Manual.
Drop or Withdraw:
• Last day to drop the course is Sept 11, 2006 (Nov 21 “for SERIOUS and COMPELLING REASONS”).
Please see me or your academic advisor to work out solutions to avoid this.
University Policies on Students with Disabilities
Students with Disabilities: Upon identifying themselves to the instructor and the university, students with disabilities will receive
reasonable accommodation for learning and evaluation. For more information, contact me and the office of Services to Students
with Disabilities in Madden Library 1049 (278-2811).
University Policies on Cheating and Plagiarism
Cheating and Plagiarism: Cheating is the actual or attempted practice of fraudulent or deceptive acts for the purpose of
improving one's grade or obtaining course credit; such acts also include assisting another student to do so. Typically, such acts
occur in relation to examinations. However, it is the intent of this definition that the term 'cheating' not be limited to examination
situations only, but that it include any and all actions by a student that are intended to gain an unearned academic advantage by
fraudulent or deceptive means. Plagiarism is a specific form of cheating which consists of the misuse of the published and/or
unpublished works of others by misrepresenting the material (i.e., their intellectual property) so used as one's own work."
Penalties for cheating and plagiarism range from a 0 or F on a particular assignment, through an F for the course, to expulsion
from the university. For more information on the University’s policy regarding cheating and plagiarism, refer to the Schedule of
Courses (Legal Notices on Cheating and Plagiarism) or the University Catalog (Policies and Regulations)
Computers: At California State University, Fresno, computers and communications links to remote resources are recognized as
being integral to the education and research experience. Every student is required to have his/her own computer or have other
personal access to a workstation (including a modem and a printer) with all the recommended software. The minimum and
recommended standards for the workstations and software, which may vary by academic major, are updated periodically and are
available from Information Technology Services (http://www/csufresno.edu/ITS/) or the University Bookstore. In the curriculum
and class assignments, students are presumed to have 24-hour access to a computer workstation and the necessary communication
links to the University’s information resources.
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Course Schedule
Note:
1. Readings may be amended and/or supplemented by outside readings for which you will be responsible.
2. The following is a tentative schedule, subject to change depending upon how quickly or slowly we go through
particular concepts.
WEEK
WEEK 1: (31 Aug) Introduction, Overview, Pre-test, 10 rules of
data collection
WEEK 2: (7 Sep). 2) Refresher of basic statistics following pre-test.
READING
Zar 2-4; review
Krebs Ch 1
Fowler 1990
Diamond 1986
WEEK 3: (14 Sep). Experimental Design; Guidelines for Metaanalysis
WEEK 4: (21 Sep). Experimental Design; type I & II errors,
Krebs, Ch. 10
Replication
WEEK 5: (28 Sep). Presentations of Meta-analyses from literature
WEEK 6: (5 Oct). Power Analysis, Hypothesis testing,
Zar, Ch. 6; Others TBA
Transformations
MIDTERM I: (12 Oct). Half of class-time for the day.
WEEK 7: (12 Oct). T-tests; ANOVA, Contingency analysis
Appropriate section from Zar
WEEK 8: (19 Oct). Multiple comparisons; rmANOVA
Appropriate section from Zar
WEEK 9: (26 Oct). Regression, Correlation
Appropriate section from Zar
WEEK 10: (2 Nov) General Linear Models
Handouts
WEEK 11: (9 Nov). Multivariate analyses: Principal Components Handouts
and Correspondence analysis
MIDTERM II: (16 Nov). Half of class-time for the day.
WEEK 12: (16 Nov) Multivariate analyses: Ordination,
Multidimensional Scaling
WEEK 13: (23 Nov) Time Series, Autocorrelation, Spatial Stats
(Intro)
WEEK 14: (30 Nov). Nonparametric analyses, Resampling and
Randomization approaches; Bayesian approaches
WEEK 15: (7 Dec). Thesis Presentations
WEEK 16: (12 Dec) Thesis Presentations. Wrap up and review.
Wednesday 13 Dec, last day of instruction
FINAL EXAM - DEC 21.
Handouts
Handouts
Handouts
Everything
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