Whitlock, M. C. and D. Schluter. 2009. The Analysis of Biological Data. Roberts &
Co.,. Greenwood Village, CO. 700 p. Optional Text: Sokal, R. R. and F. J. Rohlf.
Statistical Methods in Plant Biology PBIO-3150 (#8143) and PBIO-5150 (#8145) Spring 2013 Instructor: Office: Telephone: Email: WWW: Office Hours:
Brian C. McCarthy Porter Hall 416B 740-593-1615
[email protected]
TA: Office: Email:
Lauren Bizzari Porter Hall 416A
[email protected]; Point person for R assignments and term paper.
Scheduling:
Lecture: Tuesday & Thursday 9:00-10:20 (Porter Hall 104) Laboratory: Tuesday & Thursday TBD (Computer Lab)
Required Text:
Whitlock, M. C. and D. Schluter. 2009. The Analysis of Biological Data. Roberts & Co., Greenwood Village, CO. 700 p.
Optional Text:
Sokal, R. R. and F. J. Rohlf. 1995. Biometry: The Principles and Practice of Statistics in Biological Research, 3rd ed. W.H. Freeman, San Francisco, CA. (Recommended for PBIO-515.)
http://www.ohio.edu/plantbio/staff/mccarthy/quantmet/quantmet.htm By appointment (email or call)
Grading: PBIO-3150
PBIO-5150
Exam-I
(25%)
Exam-I
(20%)
Exam-II
(25%)
Exam-II
(20%)
Exam-III (Final) (25%)
Exam-III (Final)
(25%)
Problem Sets
Problem Sets
(25%)
(25%)
Grad Assignment (10%)
Exams:
Exams are largely quantitative (i.e., worked problem solving), with at least half of the material coming directly from the textbook. The rationale for this is to: (1) encourage you to work through the problems in the text (both exercises and examples), (2) confirm that you have reached a correct solution, and (3) to limit the scope of material you are likely to encounter. (Note that I reserve the right to make minor alterations to the data.) Occasional short answer and/or short essay questions are likely. Exams for 3150 and 5150 will be identical and taken at the same time.
Problem Sets:
There will be ca. 6 problem sets worth 25% of your grade. Problem sets are expected to be done using the R programming language. These assignments will involve a detailed tutorial at the beginning (to teach you the necessary skills) and then an assigned problem. You must hand in your input, output, and any associated graphics. This is best done by copying all materials into a single document file. See also R webpage for class: http://www.ohio.edu/plantbio/staff/mccarthy/quantmet/Rindex.htm
Term Paper:
PBIO-5150 only: I have posted a reading list composed of ca. 20 carefully selected papers from the primary literature. The core of these papers represent some of the most important and contentious statistical issues in modern experimental ecology, evolution,
and organismal biology. The goal of these readings is to make you aware of current problems regularly encountered in research and possible solutions. Select any five (5) of these papers (presumably ones which are most closely related to your research) and construct a 2-page typewritten summary of each. The readings are posted at: http://www.ohio.edu/plantbio/staff/mccarthy/quantmet/gradread.htm. These should be combined into one 10-page report submitted in class. DUE: Thursday, 18-April-2013. Academic:
Attendance, while not formally taken, is expected for all lectures and laboratories. Based on historical data, your final grade is highly correlated to your attendance. The Code of Student Conduct should be strictly adhered to. Academic misconduct (cheating, plagiarism, etc.) will result in a grade of “F” for the course with subsequent referral to the Office of University Judiciaries. The university policies regarding the student code of conduct and academic integrity are clearly and completely summarized at:http://www.ohio.edu/communitystandards/. If you are unclear as to what these policies are, please consult the website.
Goals of Course:
The goal of this course is to introduce upper-level students in the natural sciences to the quantitative skills and technological tools necessary to evaluate the literature and be able to carry out original research in the discipline. Lectures will focus on the quantitative skills associated with proper experimental design, statistical analysis, data interpretation, and presentation of results. Numerous worked examples and problem sets will be used to demonstrate and enhance your understanding of important biostatistical procedures. Laboratories will emphasize micro-computer technology and software applications likely to be encountered in the biological sciences. The goal is to provide an overview of common software applications and technological solutions to standard research problems in biology. We will explicitly emphasize R (http://www.rproject.org/) as this has become an important development tool in applied statistics, and is freely available to you during and after this course.
Course Progress:
Please note that the development of this course is deceptively slow at first! The purpose of this is to provide a strong grounding and review of the basics (introductory level basic statistics). Much of this material is essential to latter parts of the course. By virtue of the subject material, the knowledge is cumulative (although exams are not, per se). The second half of the course will have a greater momentum with increasingly technical material (intermediate to advanced biostatistics). Be careful not to fall behind! Ask for help if you need it.
Final Exam:
Thursday, May 2, 2013, 09:00-11:00, Porter Hall 104
Lecture Topics & Reading Assignments
Week
Date†
Topic
Text
1
22-JAN 24-JAN 25-JAN
Introduction, types of data, frequency distributions
1
2 3
4
***
29-JAN 31-JAN
Graphing data, measures of central tendency, measures of dispersion, exploratory data analysis
2, 3
5-FEB
Estimation, probability
4, 5
7-FEB
Hypothesis testing, proportion data
6, 7
12-FEB 14-FEB
Chi-square analysis
8, 9
19-FEB 21-FEB
Normal distribution, departures from normality, inference
10, 11
26-FEB 28-FEB
***
5-MAR 7-MAR
5 6
7
8 9
10
Review Exam-1 (descriptive stats, hypothesis testing, freq data) SPRING BREAK
12-MAR 14-MAR
Two-sample tests (parametric)
12
19-MAR
Two-sample tests (non-parametric)
13
21-MAR
Experimental design
14
26-MAR
Analysis of variance (ANOVA), F, planned comparisons
15
28-MAR 2-APR
Estimation of variance, ANOVA assumptions, tests for homogeneity of variance, normality, transformations
15
4-APR
***
Exam-2 (2-sample tests, ANOVA)
9-APR
Correlation, parametric & non-parametric
11-APR
NO CLASS (Work on Term Paper)
16-APR
Linear regression, residual analysis
17
16-APR
Non-linear regression
17
17-APR
The general linear model (GLM), analysis of covariance (ANCOVA)
18
23-APR
Completely randomized factorial designs
18
25-APR
Random- and mixed-effects ANOVA, ANOVA models with 18 block effects, repeated measures
30-APR
Bayesian statistics, bootstrapping, jackknifing
2-MAY
†
Last day to drop/add
***
Final Exam (09:00; Porter Hall, Rm. 104)
Dates are approximate and may vary based upon pace of class
16
19
