Course Syllabus for

PlSc 724 - FIELD DESIGN I

Fall Semester - 1997



INSTRUCTOR: Rich Horsley

Office 370H Loftsgard Hall or 109 Waldron Hall

Phone 237-8142

e-mailhorsley@badlands.nodak.edu



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



STATISTICAL REVIEW

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



PLANNING EXPERIMENTS



Types of experiments

Items to consider in planning experiments

Experimental units

Replication

Choice of design

Randomization



HYPOTHESIS TESTING



Type I error

Type II error

Power of the test

Steps in testing hypotheses

Testing the hypothesis that 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

Orthogonal contrasts



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

Simple correlation

Transformations

Curve fitting



DIFFERENT ARRANGEMENTS USED IN EXPERIMENTAL DESIGNS



Factorial Arrangements

Split plot arrangements

Split block arrangements

Split-split plot arrangements



COMBINING EXPERIMENTS



Combining experiments across locations

Combining experiments across years

Combining experiments across time and space