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 		PLP 222

(2 units). Experimental Approaches in Plant Pathology. Epstein. Winter-even years. Includes a plant pathology-specific discussion about research strategies, experimental designs, and data management, analysis and presentation. Emphasis on the following: data analysis of a range of plant pathology data sets including disease incidence and severity; situations in which ANOVA is inappropriate; and useful methods for hypothesis testing including linear regression, logistic regression and categorical data analysis. (Offered TR from 1:10-2 Winter 2004).

Course goals. 1) To utilize accessible research examples to teach critical concepts of experimental design and statistical analysis; 2) to assist students in their ability to design experiments, particularly in the field, and to manage and analyze their data in an appropriate and informative way; and 3) to broaden students' skills in critical thinking, quantification, data analysis, and communication of their results.

The course will rely largely upon papers, primarily from Plant Disease, as a major source of questions, hypotheses and data for our discussions in class. That is, statistical concepts will be discussed in a context of research rather than as an abstraction. Thus, the following topical outline includes topics that will be discussed, but not necessarily the order in which they will be discussed.

Topical outline

  1. Introduction
    1. Defining questions and hypotheses
    2. Advantages and disadvantages of different research strategies
    3. Experimental versus observational studies
    4. Continuous versus categorical (e.g., disease incidence and severity) response variables
  2. Hypothesis testing
    1. Null hypotheses, statistical models, & the meaning of P values
    2. Testing the desired hypothesis
  3. Critical design issues in a world with practical limitations
    1. Randomization
    2. Replication and independence of errors
    3. Common confounders
  4. Preliminary assessment of data
    1. Assessment of data quality; error checking
    2. Preparation of spreadsheets for data analysis
    3. Techniques for exploratory data analysis
  5. Does your data set meet the criteria for ANOVA?
    1. Assumptions of ANOVA and how to test for them
    2. Examples of useful transformations for plant pathology datasets
    3. When are multiple range tests appropriate and inappropriate?
    4. Examples of typical datasets that do not meet the assumptions of ANOVA
    5. Consequences of using ANOVA for a randomized complete block design with inappropriate datasets
  6. Informative descriptive statistics including confidence intervals
  7. Underutilized methods for hypothesis testing
    1. Linear regression
    2. Logistic regression
    3. Categorical data analysis (see next section)
  8. Categorical data analysis
    1. When to use
    2. Tests of independence
      1. Non-ordinal data
      2. Ordinal data
    3. Log linear models
  9. Data presentation
    1. Guidelines for graphics and tables
    2. Organization of presentations and written documents; scientific writing and text editing
  10. Professional ethics
  11. Selected methods in experimental plant pathology, particularly with fungi (if time permits)

Grading percentages and course requirements
Midterm          15%
Final          20%
Five homework exercises          55%
Class participation          10%

For the homework exercises, there will be problem sets that will require statistical analysis of data, interpretation of results, and the design of new experiments. For class demonstrations and homework assignments, I will use Excel as a spreadsheet and JMP 5 for statistical analysis. Students who prefer to use a package other than JMP 5 should check with me (SAS is fine).

Reading
A reader will be compiled.

If you are potentially interested in taking the course, would you please send Lynn Epstein, lepstein@ucdavis.edu, (754-7916) the following information:

  1. Your name and contact information
  2. Any previous courses in statistics
  3. Whether you want to use a package such as SAS that you already have access to. If so, indicate the software, and do not bother with the remaining questions.
  4. Whether you currently have access to JMP 5
  5. If you don't currently have access to JMP 5, whether you would want to purchase a personal copy (or have your laboratory purchase a copy) of JMP 5.
    Notes on JMP 5:
    I don't have current price information, but last year the UC Davis price was $72. For systems requirements for JMP 5, go to http://www.jmp.com/support/techsup/jmp5_sys_requirements.shtml Note that JMP 5 is not compatible with early versions of Windows 98 or with earlier operating systems.
  6. Would you want to have access to JMP 5 in the departmental computer laboratory?

 


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This page last updated:    May 06, 2004