Data Analysis
Data Analysis
Basic statistical methods, with an emphasis on manufacturing applications. Topics include sampling guidelines, design of experiments, analysis of variance, regression analysis, and model selection. Introduction to using Excel spreadsheets, Access databases, and VBA programming for data analysis.
| Hours | 3.0 Credit, 3.0 Lecture, 0.0 Lab |
| Prerequisites | STAT 201 |
| Taught | Fall, Spring Contact Department |
| Programs | Containing MFGEN 401 |
Course Outcomes:
Statistics & Data Analysis Tools
Students will understand the roles of descriptive & inferential statistics in manufacturing and demonstrate the ability to collect and analyze data according to statistical best practices.
Design of Experiments
Students will understand the advantages of controlled factorial experiments and demonstrate the ability to set up and draw conclusions from factorial experiments.
Regression Analysis
Students will understand and be able to correctly use ANOVA, simple and multiple linear regression, and model fit assessment tools.
Semester Project
Students will apply data analysis best practices to an open-ended engineering problem and professionally communicate the experiment, analysis, and recommendations.