OS3604 Statistics and Data Analysis

An introduction to statistics and data analysis for students in the operational curricula. Topics include point and interval estimation, hypotheses testing, analysis of variance, multiple regression techniques, and categorical data analysis. Emphasis is placed on decision rules and in the analysis of data sets from operational environments. Computations are done in a statistical analysis package.

Prerequisite

A course in probability (OS2103 or EC2010 or equivalent)

Lecture Hours

Lab Hours

Course Learning Outcomes

Recognize differences between data formats
Understand the difference between good and bad data summarization based on their format
Develop skill in both graphical and numerical methods for data summarization
Understand basic concepts of random sampling
Demonstrate basic understanding of what constitutes a statistical experiment
Understand how the concept of randomness applies to analyzing data
Recognize the most widely used probability models in statistical applications and their properties
Understand what a sampling distribution is and why it is important
Develop a working knowledge of basic statistical inference techniques for sample means, variances, and proportions
Understand how to use regression to develop a predictive model
Understand how to use categorical data analysis for testing goodness-of-fit and independence of categorical variables