Biometry

(Biology 302)

Instructor:  Dr. Jim McGraw

Office Hours:  Monday and Tuesday, 1:00 - 2:00 PM.  In any office hours, I will be available to help you in the computer lab to facilitate one-on-one questions and answers: Please come find me in my office if you would like help in the computer lab.  Other office hours can be made available to fit our schedules.  Matt Kaproth, the TA for this course, will set his own office hours and we will inform you of those shortly.

Lecture:  MWF 8:30 AM

Room:  3306 Life Sciences Building (Computer Lab)

Book:  Sokal, R. R., and F. James Rohlf.  1995, Biometry.  Freeman.  3rd Edition.

Note:  Other readings will be assigned on an as-needed basis.

In addition, for Graduate Students:  Gotelli, N. J., and A. M. Ellison.  2004.  A Primer of Ecological Statistics.  Sinauer. [$35.98 with free shipping at Amazon.com; used available as well for less)

Computer Supplies: The computer lab is outfitted with top-of-the-line imac computers courtesy of your lab fees and the Eberly College of Arts and Sciences.  You may wish to purchase your own Mac or IBM-compatible Zip Disk or, even better, usb jump (jmp!) drive with which to back up your homework and/or allow you to work in another lab.  You may store your work in a designated folder on your selected computer in the lab as well, but with my own important work, I always follow the sophisticated admonition – “BACK UP, BACK UP, BACK UP!”

Course Description:  Biometry is the application of mathematical and statistical principles to biological problems.  This course reviews the fundamental mathematics required to solve many biological problems and the basic statistical techniques used in Biology, employing a user-friendly MacIntosh computer program called SAS JMP (v. 5).  Students interested in performing, reading and understanding the analysis of actual research data will benefit from this course. Students interested in a thorough grounding in the theoretical basis of the standard statistical tests used in this course are encouraged to enroll in the appropriate courses in the Statistics Department.

Grading Policy: 

Exams.  There are none!  You learn statistics by using statistics, not by cramming for an exam, whereafter you forget what you’ve learned.  Therefore, I have chosen to grade almost entirely based on homework problem sets.

Homework.  Homework problem sets will be given out weekly on Wednesday, and due the following Wednesday in class.  Ninety percent of the grade for the class will be based on these homework problems, which expect application of the knowledge accumulated throughout the course.  The grade scale will be:  A: 90 - 100%,  B: 80 - 89.9%, C: 70-79.9%, D: 60 - 69.9%, F: <60%.  Grades will not be ‘curved’.  Homeworks are to be done on your own.  Late homeworks lose 20% per day.

            Some homework sets will contain challenge questions; these especially difficult questions go beyond what we have talked about in class and challenge you to explore Sokal and Rohlf in depth, as well as the JMP manuals, to go to the next level of understanding of biometry.  Answering challenge questions is optional for undergraduate students, but required for graduate students.  Points earned on challenge questions will be considered bonus points for undergraduates.   

            Bonus (carrots!): Go to Dr. McGraw’s office hours, ask a Biometry question, and turn in an ‘Ask-a-statistician card’ to earn a 1% added to your final grade.  Three card maximum per student (see last page of syllabus to find your cards!).

Penalty (sticks!): The remaining 10% of the grade for this course is based on attendance, with a decrement of 1% (out of 10%) for each unexcused absence.  This subject matter builds on previous material, and therefore you can become hopelessly lost if you do not attend class.  8:30 is early for some students, but get to bed early on Sunday, Tuesday, and Thursday nights, drink coffee, or do whatever it takes to get here!  You will be glad you did!  If any student misses more than 10 classes, I will recommend you drop the course.  Excused absences:  You must bring a written excuse explaining the legitimacy of the reason for your absence.  Acceptance of the excuse is at the discretion of the instructor.

More carrots:  Attendance is taken daily.  If our mean attendance is 90% or higher, I will drop the lowest homework grade of every student in the class!

 

Policy on Social Justice:  West Virginia University is committed to social justice. I concur with that commitment and expect to maintain a positive learning environment based on upon open communication, mutual respect, and non-discrimination. Our University does not discriminate on the basis of race, sex, age, disability, veteran status, religion, sexual orientation, color, or national origin. Any suggestions as to how to further such a positive and open environment in this class will be appreciated and given serious consideration.

If you are a person with a disability and anticipate needing any type of accommodation in order to participate in this class, please advise me and make appropriate arrangements with Disability Services (293-6700).  My office hours are the best times for this discussion.

Asking Questions in Class:  Please do!

Class Protocol:  Students are expected to recognize the rights of others of access to an environment that is conducive to learning.  Talking, reading the newspaper, checking email, surfing the web, cell phone calls, coming in late, walking in front of the class, or other behavior which is discourteous or disruptive to intellectual exchange is unacceptable and can lead to an administrative drop.  One incidence of cheating or plagiarism will result in a strong warning and a grade of zero for that homework set.  A second instance of cheating or academic dishonesty will result in assignment of a grade of F in this course.  Three instances will trigger the assignment of an ‘unforgiveable F’.

Evacuation Plan for Room 3306:  In the event of an emergency, leave the classroom in an orderly manner out the front door (by the stairs).  Cross the 3rd floor lobby, passing by Provost Lang’s portrait, and descend the stairs to the ground floor.  Leave the building through the nearest outside door.  Once you have left the building, quickly move as far away as possible while avoiding parking lots.  Do not congregate near the building or in parking lots.

Course Outline

1.  Course overview and basic definitions

2.  Simple math functions         Batschelet. Intro to Mathematics for Life Sciences           

                                                                        HA201 1950 .A257 (Evansdale)                       

            a.  principles of multiplication and division

            b.  notation for summation

            c.  powers

            d.  linear functions

            e.  power functions

            f.  polynomials

            g.  periodic functions

            h.  logarithms

 

3.  The nature of data                                                                       

                                                                                               

                                                                        S&R Chaps. 1 + 2, G&E Chap. 1, 2,8

            a.  Observations

            b.  Sample

            c.  The ‘population’

            d.  Biological variables; examples from cell, organismal, and ecological studies

                        -Distributions

                                    •continuous

                                    •ordinal

                                    •nominal

                        -Roles

                                    •Dependent

                                    •Independent

 

4.  Descriptors of biological groups                                               

                                                                        S&R Chap. 4, G&E Chap. 3

            a.  mean

            b.  variance

            c.  standard deviation

            d.  standard error

            e.  confidence limits

                                   

5.  The simplest statistical test(s)                        S+R p. 223-229, Chap. 13, G&E Chap. 4

                                                                                               

            a.  t-test

                        -test whether the mean = a value

                        -test whether two samples could have been drawn from a population with the same mean

            b.  Interpreting p-values associated with tests

            c.  Observing ‘residuals’

            d.  Testing normality of data

                        -testing with Shapiro-Wilk W test

                        -transformations to improve normality

 

6.  Classes of statistical tests (Nominal and continuous dependent and independent variables)

 

                                    Continuous Y, Nominal X

 

7.  Population comparison or ANOVA made easy. S&R Chaps. 8 – 13, G&E, Chap 6,7,10

                                                                                               

 

            a.  1-way ANOVA

                        -  The F-test

                        -  1-way ANOVA; the calculations

                        -  Type I and Type II ‘error’

                        -  Multiple comparisons tests; which means are different from which others?

                        -  Planned comparisons

            b.  2-way ANOVA

            c.  3-way and higher-order ANOVA

            d.  Nested ANOVA

            e.  Mixed model ANOVA

                        -Fixed effects vs. random effects; contrasting types of independent variables

                        -Expected means squares; How to select proper error terms for testing effects

 

                                    Continuous Y, Continuous X

 

8.  Regression                                                                        S&R Chap. 14, 16,  G&E Chap. 9

                                                                                               

            a.  Linear regression

            b.  Causation and the meaning of regression

            c.  Polynomial regression

            d.  Multiple regression

            e.  Quadratic regression

 

                                    Nominal Y, Nominal X

 

9.  Contingency analysis                                                S+R Chap. 17, Chap. 11

            a.  One-way contingency; G-test (loglikelihood)

            b.  Multi-way contingency

 

                                    Nominal Y, Continuous X

 

10.  Logistic regression                                                            S+R p. 768-778

                                                                                               

 

                                    Y vs. Y

 

11.  Tests of association                                                            S+R Chap. 15, 17

            a.  Both continuous Y; correlation (r, r2)

            b.  Both nominal Y; goodness-of-fit

 

12.  Special techniques for misbehaving or flummoxing data            S+R Chap. 18

            a.  The jackknife

            b.  The bootstrap

            c.  Other distribution-free methods

 

 

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