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Author
Sagan Adam (Uniwersytet Ekonomiczny w Krakowie / Kolegium Nauk o Zarządzaniu i Jakości)
Title
Krzywe operacyjno-charakterystyczne w ewaluacyjnych badaniach marketingowych
The Receiver Operator Characteristic Curve in Market Research Evaluations
Source
Zeszyty Naukowe / Uniwersytet Ekonomiczny w Krakowie, 2011, nr 864, s. 5-17, rys., tab., bibliogr. 8 poz.
Keyword
Badania marketingowe, Analiza marketingowa, Promocja produktu, Kampanie społeczne, Samochody osobowe
Marketing research, Marketing analysis, Product promotion, Social campaigns, Motor cars
Note
summ.
Abstract
Krzywe operacyjno-charakterystyczne są popularnym narzędziem wizualizacji, oceny i wyboru modeli predykcyjnych i klasyfikacyjnych. Przedstawiają one graficzny obraz zależności między korzyściami a kosztami klasyfikacji. Przedstawiono charakterystykę, a także zastosowanie tych krzywych w ocenie wyrobów samochodów osobowych. Omówiono również wskaźniki diagnostyczne dla tabeli 2x2.

The articles examines the characteristics and use of the receiver operator characteristic (ROC) in research on marketing phenomena. This approach is an extremely popular tool for assessing prediction accuracy in medical research and signal detection, and is being ever more widely used in social and marketing research. The article presents the main indicators of classification accuracy for contingency tables (2x2) and the principles governing the use of ROC curves. The use of these curves is illustrated with an analysis of the accuracy of the choice of car (new or used) based on the structure of customer benefits. (original abstract)
Accessibility
The Main Library of the Cracow University of Economics
The Library of Warsaw School of Economics
The Library of University of Economics in Katowice
The Main Library of Poznań University of Economics and Business
The Main Library of the Wroclaw University of Economics
Full text
CUE campus access only
Bibliography
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  1. Davis J., Goadrich M., The Relationship between Precision-Recall and ROC Curves, Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, PA 2006.
  2. Egan J.P., Signal Detection Theory and ROC Analysis, Academic Press, New York 1975.
  3. Fawcett T., An Introduction to ROC Analysis, "Pattern Recognition Letters" 2006, nr 27.
  4. Glady N., Bart Baesens B., Croux C., Modeling Customer Loyalty Using Customer Lifetime Value, Catholic University Leuven, www.econ.kuleuven.be/fetew/pdf_publicaties/KBI_0618.pdf, 11.01.2008.
  5. Maxion A., Roberts R.R., Proper Use of ROC Curves in Intrusion/Anomaly Detection. Technical Report Series, University of Newcastle upon Tyne, 2004.
  6. Stein R.M., The Relationship between Default Prediction and Lending Profits: Integrating ROC Analysis and Loan Pricing, "Journal of Banking & Finance" 2005, nr 29.
  7. Stephan C. i in., Comparison of Eight Computer Programs for Receiver-Operating Characteristic Analysis, "Clinical Chemistry" 2003, nr 49(3).
  8. Vukl M., Curk T., ROC Curve, Lift Chart and Calibration Plot, "Metodoloski zvezki" 2006, nr 3(1).
Cited by
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ISSN
1898-6447
Language
pol
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