The Importance of Statistical Modeling in Data Analysis and Inference

Authors

  • Derrick Rollins, Sr. Iowa State University • Ames, Iowa 50011

Abstract

Statistical inference simply means to draw a conclusion based on information that comes from data. Error bars are the most commonly used tool for data analysis and inference in chemical engineering data studies. This work demonstrates, using common types of data collection studies, the importance of specifying the statistical model for sound statistical analysis and inference and how this reveals unsound approaches such as the use of error bars.

Author Biography

Derrick Rollins, Sr., Iowa State University • Ames, Iowa 50011

Derrick K. Rollins, Sr. is a professor in Chemical & Biological Engineering and Statistics. He received a B.S. degree in chemical engineering from the University of Kansas, and M.S. in statistics, as well as M.S. and Ph.D. in chemical engineering, from The Ohio State University. He previously worked as a process engineer for the E.I. Du Pont Chemical Company and as a faculty intern for the 3M Company. His research areas include glucose monitoring, modeling, and control to help people with diabetes control blood sugar better, and (medical-, bio-, and material-) informatics.

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Published

2017-07-07

Issue

Vol. 51 No. 3 (2017): Summer 2017

Section

Manuscripts