Logistic regression analysis of customer satisfaction data

Cathy Lawson, Douglas Montgomery

Research output: Contribution to journalArticle

15 Scopus citations

Abstract

Variation exists in all processes. Significant work has been done to identify and remove sources of variation in manufacturing processes resulting in large returns for companies. However, business process optimization is an area that has a large potential return for a company. Business processes can be difficult to optimize due to the nature of the output variables associated with them. Business processes tend to have output variables that are binary, nominal or ordinal. Examples of these types of output include whether a particular event occurred, a customer's color preference for a new product and survey questions that assess the extent of the survey respondent's agreement with a particular statement. Output variables that are binary, nominal or ordinal cannot be modeled using ordinary least-squares regression. Logistic regression is a method used to model data where the output is binary, nominal or ordinal. This article provides a review of logistic regression and demonstrates its use in modeling data from a business process involving customer feedback.

Original languageEnglish (US)
Pages (from-to)971-984
Number of pages14
JournalQuality and Reliability Engineering International
Volume22
Issue number8
DOIs
StatePublished - Dec 1 2006

Keywords

  • Binary response
  • Business process modeling
  • Categorical data analysis
  • Logistic regression

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Management Science and Operations Research

Fingerprint Dive into the research topics of 'Logistic regression analysis of customer satisfaction data'. Together they form a unique fingerprint.

  • Cite this