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CSS 590. Experimental Design in Agriculture

Winter 2011

Lectures and Notes
Weekly Assignments
Lab Exercises
Class Exercises and Reviews
Examinations
Proposalfor Experiment
Miscellaneous Handouts
FAQ

Additional links will be activated as the term progresses.

Announcements will be posted on the Blackboard as needed.

Time and Place:
Lectures: MWF 8:00 CRPS 122
Recitation: Thurs 8:00 - 9:20 Cordley 4056
Help Sessions (optional, on request): Tuesday 8:00-9:00 CRPS 150

Instructor:Jennifer Kling
Telephone: 737-8277
E-mail:
Office: CRPS 249
Office Hours: MWF 9:30-10:30 - others by appointment

Teaching Assistant: Yada Chutimanitsakun
Telephone: 541-737-5876
E-mail:
Office: CRPS 221
Office Hours:Thursday 10:00 am

Required Text

There is no required text for this class. Recommended references are on reserve in the library.

Recommended References

Kuehl, Robert O. (2000) Design of Experiments: Statistical Principles of Research Design and Analysis, 2nd edition. Duxbury Press. (an excellent general reference, but uses slightly different notation than we do in class) Q182.3.K84 2000

Clewer, A.G. and D. H. Scarisbrick (2001) Practical Statistics and Experimental Design for Plant and Crop Science. John Wiley & Sons. (A good reference for those who are not too familiar with statistics; the emphasis on agricultural research is also very relevant for the course) QK51.C58 2001

Cody, Ronald P. and Jeffrey K. Smith (2006) Applied Statistics and the SAS Programming Language, 5th edition. Prentice Hall, New Jersey. QA276.4 .C53 2006

Petersen, Roger G. (1994) Agricultural Field Experiments: Design and Analysis. Marcel Dekker, New York. (Much of the lecture material was adapted from this text, but it contains some errors. Two copies of the text are on reserve in the library, should you need any clarification on lecture material.) S540.F5 P47 1994

Other Suggested References:

Cochran, W. G., and G. M. Cox (1957). Experimental Designs, 2nd ed., Wiley, New York.

Cox, D. R. (1958). Planning Experiments, Wiley, New York.

Gomez, K. A. and A. A. Gomez (1984). Statistical Procedures for Agricultural Research, 2nd ed. Wiley, New York.

Little, T. M., and F. J. Hills (1978). Agricultural Experimentation, Wiley, New York.

Montgomery, Douglas C. (1991). Design and Analysis of Experiments, 3rd ed., Wiley, New York.

Petersen, R. G. (1985). Design and Analysis of Experiments, Marcel Dekker, New York.

Ramsey, F.L. and D.W. Schafer (2002). The Statistical Sleuth: A Course in Methods of Data Analysis, 2nd ed., Brooks/Cole, CA.

Snedecor, G. W., and W. G. Cochran (1980). Statistical Methods, 7th ed., IowaStateUniversity Press, Ames, IA.

Steel, R. G. D., J. H. Torrie and D.A. Dickey (1997). Principles and Procedures of Statistics, 3rd ed., McGraw-Hill, New York.

Other SAS References:

Der, G. and B.S. Everitt (2002). A Handbook of Statistical Analyses using SAS, 2nd ed., Chapman & Hall/CRC.

Littell, R. C., W. W. Stroup, and R. J. Freund (2002). SAS for Linear Models, 4th ed. SAS Series in Statistical Applications.

Online Resources

Announcements will be posted on the Blackboard as needed.

For information on how to access and use the Blackboard, go to the OSU Extended Campus website login page at

Grades for homework and exams will be posted on the Blackboard under CSS_590_001_W2011.

You can access the course website through the Blackboard (click on the course information button) or directly at this url:

General Course Description
This course addresses the needs of the student preparing for a career in agricultural research or consultation and is intended to assist the scientist in the design, plot layout, analysis and interpretation of field and greenhouse experiments. Emphasis is placed on experimental designs used in agronomy and plant breeding research with more emphasis toward applied statistics rather than statistical theory. Many numerical examples and problems will be presented and the recitation will allow students to explore analysis using SAS and Excel.

Prerequisites

Students should have an introductory understanding of statistical methods including the ideas of interval estimation, significance testing, simple linear regression and correlation. Familiarity with such common statistical tables as Student’s t, F, and chi-square is expected. The necessary mathematical background is minimal. At most, a knowledge of college algebra is required.

Assessment/Evaluation of Student Performance

Weekly assignments / 20%
Recitations / 10%
First exam / 10%
Second exam / 15%
Take-home design problem and SAS analysis / 15%
Proposal for experiment / 15%
Final exam / 15%
Total / 100%

Grades will be assigned according to the following point system:

97-100 = A+ / 87-89 = B+ / 77-79 = C+ / 67-69 = D+ / <=59 = F
93-96 = A / 83-86 = B / 73-76 = C / 63-66 = D
90-92 = A- / 80-82 = B- / 70-72 = C- / 60-62 = D-

Assignments

There will be eight graded assignments in this class, which will be due one week after they are assigned. It is highly recommended that you use Excel to complete the assignments, and that you submit them electronically via the Assignment function in Blackboard.

Recitations

You will have the opportunity in Recitation to gain some hands-on experience with SAS analyses. You will not be expected to turn in work from your recitation sections. Self-assessments will be available on Blackboard to encourage you to review the lab material and ensure that you have understood the major concepts presented each week. You will also need to be able to write simple SAS programs in order to do your take-home design problem, and you may be asked to interpret SAS output on exams.

Exams

You may bring one 8.5” x 11” piece of paper and a calculator with you to each exam. You may write any formulas or notes that you think you will need on the front and back of the paper. Exams are comprehensive, but emphasis will always be on the material since the last exam. For the second midterm and final, you may bring the 8.5” x 11” papers that you prepared for earlier exams, along with a new sheet for the exam that you are taking.

Instructional Objectives and Student Learning Outcomes:

Upon completion of the course, students should be able to:

  • Define experimental error
  • Enumerate and explain at least five ways that experimental error can be reduced
  • Discuss the relationship of plot size, shape, and placement to experimental error
  • Identify objectives of a field experiment
  • Summarize and give examples of the assumptions of the ANOVA. Use statistical tools to detect violations of ANOVA assumptions and apply appropriate transformations
  • Discuss the difference between sampling, replication and blocking
  • Explain and compute the components of an analysis of variance for the following experimental designs and list the advantages and disadvantages of each:
  • Complete randomized
  • Randomized complete block
  • Latin square
  • Split plot
  • Strip plot
  • Augmented
  • Define and explain the applications of some Incomplete Block Designs
  • Explain the difference between fixed and random effects and be able to interpret computer output from mixed model analyses
  • Select and justify a particular experimental design to meet the experimental objectives
  • Use Excel to generate random numbers, organize and summarize data, display data graphically and perform simple statistical analysis
  • Explain the difference between main effects and interactions and how these affect the interpretation of results
  • Form appropriate orthogonal contrasts to answer specific questions raised by an experiment
  • Demonstrate the appropriate use of mean separation techniques
  • Set up an ANOVA table for multilocational or multiyear trials and explain how the F tests would be computed
  • Use SAS to generate an analysis of variance for all of the experimental designs discussed

Outline of Topics Covered

  • Week 1 Basic Principles
  • Kinds of field experiments
  • Site selection
  • Experimental error
  • Field uniformity
  • Steps in experimentation
  • Types of data to collect
  • Hypothesis testing
  • Review of t tests
  • Week 2 Basic Experimental Designs
  • The importance of randomization
  • Completely randomized design
  • Control of experimental error
  • Concepts of replication and blocking
  • Randomized block design
  • Week 3 The Field Plot
  • Plot shape and orientation
  • Border effects
  • Optimum and convenient plot size
  • Number of replications and power calculations
  • Week 4 Refining the Model
  • First test
  • ANOVA assumptions
  • Checking ANOVA assumptions and transformations
  • Fixed, Random, and Mixed Models
  • (Introduction to Generalized Linear Mixed Models)
  • (Analysis of Covariance)
  • Week 5 More Designs + Factorial Experiments
  • Subsampling
  • Latin square design
  • Factorial Experiments
  • Main effects and interaction
  • Week 6 Contrasts
  • Orthogonal contrasts
  • Regression in the ANOVA
  • Orthogonal polynomial contrasts
  • Week 7 Different Plot Sizes within an Experiment
  • Second test
  • Split-plot designs
  • Strip-plot designs
  • Repeated measures
  • Week 8 Comparison of Means with Unstructured Treatments
  • Take-home design problem and SAS analysis
  • Structured versus unstructured experiments
  • Mean separation techniques
  • Week 9 Large Numbers of Treatments
  • Augmented designs
  • Incomplete block designs
  • Lattice designs
  • Week 10 Experimental Design in the Real World
  • Multilocational trials
  • Student proposals - poster session, Wed., March 9, 2011
  • Review
  • Exam Week
  • Final - Thursday, March 17, 2011 at 6:00 pmin CRPS 122

Tentative Recitation Schedule

Week 1 Introduction to SAS; review of basic statistics
Week 2 One-Way ANOVA (CRD)
Week 3 Two-way ANOVA (RBD)
Week 4 ANOVA Assumptions, Transformations, Mixed Models
Week 5 Subsampling, Latin Square ANOVA
Week 6 Factorials and Contrasts
Week 7 Split Plot, Strip Plot, Repeated Measures
Week 8 Multiple Comparison Tests
Week 9 Incomplete Block Designs
Week 10 Across Site Analyses