Corequisite or Prerequisite 64223 Statistics II, or permission of the instructor.

Description

This course is designed as an honor component to supplement the existing Statistics II course. Students will become familar with the concepts and techniques of experimental design within the context of a research situation. Students will learn how to use Statistical Analysis Software (SAS) to analyze and interpret the outcome of the design.

Statement of Course Need

Honors courses in mathematics have been developed to provide mathematically talented students the opportunity to obtain a level of rigor not currently available in existing courses. Topics in these courses have been selected to help students develop an appreciation opf the origin and evolutionary growth of mathematical ideas from antiquity to the present. These courses have been designed as one credit components to existing courses. They are intended to both supplement and complement the ideas and topics presented in courses at the level of precalculus, calculus, and statistics

Place of Course in College Curriculum

1. Satisfies general education requirements in mathematics and science.
2. Serves as one-credit of a mathematics elective for all programs.
3. Serves as one-credit of an honors elective in mathematics.

1. Understand the role of statistical inferences in experimental design.
2. Present well-organized procedures for applying design concepts and techniques.
3. Formulate specific research questions that can be answered.
4. Analyze research articles and journal entries from different academic fields, including the student's own.
5. Detect the misuse of statistical techniques.
6. Design and conduct an original experiment, including data collection, implementation of appropriate design, SAS analysis and interpretation.

1. Analysis of Variance
   1. Terminology
   2. Controlling information in an experiment
   3. The logic behind an ANOVA
   4. Completely randomized designs
   5. Randomized blocks
   6. Two-factor factorial experiments
   7. The relationship between Anova and regression
   8. k-way classifications
   9. Checking assumptions
2. Nested Sampling
   1. Two-stage nested design
   2. Three-stage nested design
   3. Estmating a population mean based on nested sampling
   4. Underlying theory
   5. Comparing two or more populations using nested sampling
3. Statistical Analysis Software (SAS)
   1. Entering data
   2. Writing commands
   3. Interpreting and analzing output

Methods for student Evaluation
Will vary according to the instructional style of the teaching faculty.
Lab Fees--None; Date: Jan, 1997, Sponsoring Dept: Mathematics