Logistic regression is a valuable statistical tool used to model the probability of a binary response variable as a function of one or more input variables. The goal of this thesis research is to develop a better understanding of how the coefficients of a logistic regression model influence the probability of a response. Typically, the odds ratio is used for this, but this research focuses on the steepness of logistic curve near the median quantile. In order to study this, a web application using R Shiny was developed to simulate a logistic regression function based on a single continuous input variable. The web application allows a variety of inputs to be manipulated, including sample size, noise structure, amount of noise, and actual parameter values. An example using the NASA O-Ring data is illustrated as motivation and discussion.

Library of Congress Subject Headings

Logistic regression analysis--Computer simulation

Publication Date


Document Type


Student Type


Degree Name

Industrial and Systems Engineering (MS)

Department, Program, or Center

Industrial and Systems Engineering (KGCOE)


Rachel Silvestrini

Advisor/Committee Member

Scott Grasman


Physical copy available from RIT's Wallace Library at QA278.2.H67 K47 2017


RIT – Main Campus

Plan Codes