Binary responses are routinely observed in practice whether it is medicine, geology, defense or day to day life situations. Logistic regression methods can be used to capture the binary responses. Modeling becomes critical when there is sensitivity analysis involved, and the selection of the settings of variables depends on sequential design methodology. A total number of experimental runs is also an important factor since cost is directly related to it. In this research different experimental approaches for logistic regression modeling are investigated to improve the estimation of median quantile, to reduce the number of experimental runs as well as to improve overall modeling quality. We present the Break Separation Method which guarantees an overlap in the data such that the Maximum Likelihood Estimation may be used to estimate the model parameters. We also investigate and discuss the augmentation after the BSM.

Library of Congress Subject Headings

Logistic regression analysis; Sequential analysis

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

Brian Thorn


Physical copy available from RIT's Wallace Library at QA278.2.H67 T43 2017


RIT – Main Campus

Plan Codes