Quantitative Methods in Applied Biology

PLNTPATH 655    No. 16904-4

Cross-listed with ENTOMOL 655



Brian McSpadden Gardener, Assistant Professor, Dept. of Plant Pathology, 214 Selby Hall,

OARDC, Wooster (330) 202-3565,  bbmg+@osu.edu  (lectures in blue)

Casey Hoy, Professor, Dept. of Entomology, 203B Thorne Hall, OARDC, Wooster  (330) 263-3611, hoy.1@osu.edu (lectures in black)



Prerequisite:   STAT 528 or equivalent with instructor approval. Students must have an e-mail account and access to a personal computer with statistical software.


Credit:  3 credit hours

Lecture and Discussion:   MWF 11:00 AM to 12:00 noon, Fisher 121 / Kottman 244


Course Descrption and Objectives:  This course provides a review of quantitative methods commonly used in biological research, particularly agricultural and environmental sciences.  The nature of biological data requires that particular attention be paid to model formulation and analysis.  The course focuses on the methods used to describe typical data collected in biological experiments at the molecular, cellular, organismal, population, and ecosystem levels.  The use of

standard statistical methods (e.g. comparison, correlation, regression) for single and multivariate cases will be reviewed.  Application of such methods to experimental design will be highlighted.  Additional material will cover clustering and ordination methods and their use in taxonomy and phylogenetic analysis. 


After taking the course students should have the ability to:

            independently analyze data from  their own experiments

            use statistical software and interpret the output

            evaluate quantitative methods used in other's research

            communicate effectively with statistical consultants and researchers in quantitative fields of study

            participate in more advanced quantitative methods courses.



Topical Outline                                                                                   Reading Assignment


9/22 -9/27

Scientific knowledge and the need for quantitation  (3)                      S&G Chapter 1

How does scientific knowledge accumulate?

How do quantitative methods help?

From observations to models in biology

Building more detailed models and testing competing models

9/24 Guest lecturer: Dr. Dan Herms

HW 1 assigned 9/27 due10/1


9/29- 10/4

Finding and describing patterns in biological observations (3)   F Ch.1, S&G Ch.3, MG&L

Types of data

Types of variables

Understanding populations from sample data

Graphical descriptions of sample data

Describing distributions numerically

HW2 assigned 10/1 due 10/6



Statistical tests and assumptions  (2)                                                            previous readings

Inferential statistics

Testing assumptions

Testing hypotheses

HW3 assigned 10/4 due 10/8



Generalized linear modeling and treatment structures (5)             F Ch 3, 4, S&G Ch.4,6

Linear models for quantitative variables

Relationships between correlation and regression

Linear models for qualitative variables

Combining quantitative and qualitative variables

HW4 assigned 10/11 due10/15

HW5 assigned 10/18 due 10/22


Mid-Term Review 10/25

Mid Term Exam 10/27  1:00-2:30 pm


10/22, 10/29-11/3

Experimental design and error structures (4)                            S&G Ch 2, previous readings

Applying error analyses and parameter estimation


Blocked and nested designs

Random and Fixed effects

HW6 assigned 11/1 due 11/5



When assumptions don't hold (4)                                                S&G Ch  8, previous readings

Incomplete data sets and unbalanced designs

Replication and pseudoreplication

Repeated measures

Transformations of data to meet assumptions

HW7 assigned 11/8 due 11/12



Nonparametrics  - Not just another tranformation (2)                           Handouts

The idea of ranking

Differing ideas on the nature of NP statistics

Choosing and using nonparametric tests

Analyzing factorial designs of ordinal data

11/15 Guest lecturer: Mizuho Nita

HW8 assigned 11/12 due 11/19



Classification (3)                                                                                                     F 5,6    

Similarity and dissimilarity coefficients


Algorithms for clustering

Principles of ordination analyses

HW9 assigned 11/19 due 11/29



Taxonomy and phylogenetics (3)                                                      S&G 14, previous readings

Numerical taxonomy and phenetics

Cladistics and parsimony

Methods of analysis and interpretations

HW10 assigned 11/29 due 12/3


Final Exam Review 12/3

Final Exam: Thursday 12/9  7:30 -9:20 am


Required Texts


Fry, JC (editor) 1993. Biological Data Analysis, a Practical Approach. IRL Press, Oxford, England.


Scheiner, SM, and Gurevitch, J  2000.  Design and Analysis of Ecological Experiements. Chapman&Hall, London.



Additional texts on reserve:


McSpadden Gardener, BB and Lilley, AK  1997. Application of Common Statistical Tools. P. 501-523. In Modern Soil Microbiology, van Elsas, Trevors, and Wellington eds. Marcel Dekker: New York.


Sheskin, D.J. 1997. Handbook of parametric and nonparametric statistical procedures.  CRC Press, Boca Raton, FL



Grading: Scores will be given for two examinations and weekly homework assignments.

Midterm Exam                                                     20%

Final Exam (comprehensive)                               30%

Homework Assignments                                     50%


Academic misconduct:  Academic misconduct of any kind will not be tolerated.  The term academic misconduct includes all forms of student academic misconduct wherever committed: illustrated by, but not limited to, cases of plagiarism and dishonest practices in connection with examinations. (Faculty Rule 3335-5-487).  All suspected cases of academic misconduct will be reported to the University Committee on Academic Misconduct.  For this course, copying

answers from another student or using unauthorized crib notes during an examination will be considered academic misconduct.


2005 Ohio State University