**Biometry**

**(Biology 302)**

*I*

*Biometry Syllabus*

*Bio. 302/593H*

*Fall, 2012*

** Instructor: ** Dr. Jim McGraw

** Office:** Room 5204, LSB

** Office Hours:** Monday and Tuesday, 1:30 - 2:30 PM. In any office hours, I will be available in my office to help answer one-on-one questions: I am willing to come help you in the computer lab or with SAS JMP on my office computer. Other office hours can be made available to fit our schedules.

** Lecture:** MWF 8:30 AM

** Room: **3306 Life Sciences Building (Computer Lab)

** Book:** Sokal, R. R., and F. James Rohlf. 2012, Biometry. Freeman. 4th Edition.

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

In addition, for Graduate Students (Bio. 593H): Gotelli, N. J., and A. M. Ellison. 2004. A Primer of Ecological Statistics. Sinauer. ($37.99 at Amazon.com)

** Software:** You may wish to consider, for convenience, getting your own JMP software via a license ($25 for the year for students; not bad!). Here’s the web site for you: http://oit.wvu.edu/slic/softwarelist/sas/ One reason this is a good idea is that the computer lab is being shared by a number of courses (including the huge Bio 115 for testing) – therefore access to the computer lab can be dicey at times.

** Computer Supplies:** The computer lab is outfitted with top-of-the-line Mac computers courtesy of your lab fees and the Eberly College of Arts and Sciences. You will want to have your own 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 (I will let you know how in class) 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 computer program called SAS JMP (v. 9). 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. * You learn statistics by using statistics and doing statistics, not by cramming for an exam, whereafter you forget what you’ve learned. Therefore, I have chosen to assign your grade largely based on in-class quizzes, out-of-class problem sets, and attendance. **However**, this year, I have implemented a required final comprehensive homework set *for all students who have a grade of less than A*

*In-class Pop Quizzes.* To encourage you to read the material before coming to class, keep up with the material, review the material, and to become proficient at using SAS JMP yourself, I will be giving in-class, unannounced pop quizzes. The pop quizzes will take about 15 minutes to complete, will occur approximately once per week, and **count for 30% of your course grade**. Quizzes toward the end of the semester may be more comprehensive, longer, and review more material.

*Homework Problem Sets. *Problem sets will be given out approximately weekly, and due the following Wednesday in class. **Sixty percent of the grade for the class** will be based on these homework problems, which expect application of the knowledge accumulated throughout the course. 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 JMP, to go to the next level of understanding of biometry. Answering challenge questions is

Homeworks are to be done __on your own__.

*Attendance: ***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 5 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. Normally, acceptable excuses will involve serious personal medical issues or family emergencies. Acceptance of the excuse is at the discretion of the instructor.

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!).

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! (note; the dead week ‘required’ homework for <A students will not be dropped).

*Grade scale:*

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’.

*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 incident of cheating or plagiarism will result in a strong warning and a grade of zero for that assignment. 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 3^{rd} floor lobby, passing by Gerald E. 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 (with readings; S&R = Sokal and Rohlf. G&E = Gotelli and Ellison)**

1. Course overview and basic definitions

2. Simple math functions: On your own

I used to suggest checking out: Batschelet. Intro to Mathematics for Life Sciences.

But now, I suggest you consult the Khan Academy to shore up your weaknesses in any of these areas. www.khanacademy.org. There you can find a series of video minilectures to help you with topics where your high school or early college math courses (or the person in the mirror) failed you the first time you were exposed to these things!

a. principles of multiplication and division

b. notation for summation

c. powers

d. linear functions

e. power functions

f. polynomials

g. periodic functions – sine, arcsine

h. logarithms

3. The nature of data

S&R Chaps. 1 + 2, G&E Chaps. 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, median and mode

b. variance

c. standard deviation

d. standard error

e. confidence limits

f. coefficient of variation** **

5. The simplest statistical test(s) S+R 6, 13, p. 223-228, 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. Why be normal?

e. 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. 780-793

**Y vs. Y**

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

a. Both continuous Y; correlation (r, r^{2})

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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