Course Description

Covers discrete and continuous probability laws, random variables; expectations; laws of large numbers and central limit theorem; estimation, testing hypothesis, analysis of variance, regression analysis and nonparametric methods. Emphasizes applications with an introduction to packaged computer programs. Meets with APPM 4570/5570. Not for degree credit.

Outline

1. Properly classifying, statistically describing, and illustrating all forms of data
2. Understanding and utilizing probability rules and theory to formulate answers to common business/industrial/engineering decisions
3. Applying his or her understanding of data and probability to generate and understand common probability distributions, and make decisions based upon each distribution
4. Generating and mathematically describing common Random Sampling Distributions (RSDs), and applying each in order to solve problems and make decisions
5. Utilizing Random Sampling Distribution theory to conduct basic hypothesis tests to assess assumptions (one and two sample independent and dependent tests) related to proportions, counts, and measures of central tendency, dispersion, and shape in order to make accurate and cost-effective decisions
6. Describing common measures of correlation and association, and performing simple regression analysis
7. Utilizing computer-based statistical software to perform the tasks required as described by items (1) through (6, which will result in an understanding of the use and applications of this content, by minimizing time requirements associated with performing simple mathematical calculations.

Prerequisites

None.

Textbook(s)

None Required.

Hardware/Software

Web and email access required. MVPStats statistical software (http://www.mvpprograms.com/html/student.html) must be purchased.

Syllabus