Course Syllabus for
PlSc 724 - FIELD DESIGN I
Fall Semester - 1997
INSTRUCTOR: Rich Horsley
Office 370H Loftsgard Hall or 109 Waldron Hall
Course Description: PlSc 724 is a lecture course that discusses different statistical techniques for the analysis and interpretation of biological problems. Statistical techniques to be used include analysis of variance, simple linear regression, and simple correlation. Topics related to the planning of experiment to test hypotheses related to biological problems also are discussed.
Prerequisite: An introductory course in statistics
Required Text: Principals and Procedures of Statistics - A Biometrical Approach: 3rdEdition. 1997. R.G.D. Steel, J.H. Torrie, and D.A. Dickey.
Goals of PlSc 724: The broad goal for this course is to instruct students how to properly plan experiments, analyze data, and interpret results associated with testing hypotheses related to biological problems.
Outcome 1 Students will be able to comprehend concepts needed to plan experiments to test hypotheses. These concepts include experimental error, replication and its function, relative precision, error control, and randomization.
Outcome 2 Students will comprehend three experimental designs: completely random design, randomized complete block design, and latin square design. For each design, students will know: the proper randomization procedure, how to describe the design, advantages and disadvantages, how to partition total degrees of freedom and sources of variation, the linear additive model, how to write expected mean squares, how to calculate estimates for missing data, how to do the analysis of variance, how to make tests of significance, and how to interpret results of significance.
Outcome 3 Students will be able to choose the correct experimental design to test hypotheses related to biological problems.
Outcome 4 Students will comprehend the use of simple linear regression to analyze and interpret results from experiments related to biological problems.
Outcome 5 Students will comprehend the use of simple correlation to analyze and interpret results from experiments related to biological problems.
Grading: Homework - ten homework assignments (10%)
Two lecture examinations (25% each)
Final exam - Comprehensive (40%)
This course is graded on a curve. The gradelines for the curve are determined by the level of difficulty of the examinations and homework. Yet, all scores of 90% or above are guaranteed an A, and scores of 80 to 89.9% are guaranteed a B.
STUDENTS WITH DISABILITIES
PlSc 724 TOPIC OUTLINE
Types of variables
Populations vs. Samples
Three measures of central tendency
Three measures of dispersion
Variance of the mean and standard error
Coefficient of variation
Linear additive model
Types of experiments
Items to consider in planning experiments
Choice of design
Type I error
Type II error
Power of the test
Steps in testing hypotheses
Testing the hypothesis that µ is a specified value (t-test and confidence interval)
COMPARISONS INVOLVING TWO SAMPLE MEANS
Two sample means with equal variance (t-test, confidence interval, and F-test)
Two sample means with unequal variance (t-test)
COMPLETELY RANDOM DESIGN
ANOVA for any number of groups with equal replication
ANOVA for any number of groups with unequal replication
ANOVA with sampling
Linear models for CRD experiments
Assumptions underlying ANOVA
MEAN COMPARISON TESTS
Least Significant difference (lsd)
Duncan's new multiple range test (DMRT)
Testing effects suggested by the data
RANDOMIZED COMPLETE BLOCK DESIGN
ANOVA for any number of treatments
ANOVA with sampling
Linear models for RCBD experiments
Experimental error in RCBD experiments
LATIN SQUARE DESIGN
ANOVA for single square
ANOVA for repeated squares
REGRESSION AND CORRELATION
Simple linear regression
DIFFERENT ARRANGEMENTS USED IN EXPERIMENTAL DESIGNS
Split plot arrangements
Split block arrangements
Split-split plot arrangements
Combining experiments across locations
Combining experiments across years
Combining experiments across time and space