Metrics--When and Why Nonaveraging Statistics Work
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DOI: 10.1287/mnsc.1080.0907
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References listed on IDEAS
- Sunil Gupta & Valarie Zeithaml, 2006. "Customer Metrics and Their Impact on Financial Performance," Marketing Science, INFORMS, vol. 25(6), pages 718-739, 11-12.
- Steven M. Shugan, 2007. "Editorial—The Anna Karenina Bias: Which Variables to Observe?," Marketing Science, INFORMS, vol. 26(2), pages 145-148, 03-04.
- Stefan Stremersch & Peter C. Verhoef, 2005. "Globalization of Authorship in the Marketing Discipline: Does It Help or Hinder the Field?," Marketing Science, INFORMS, vol. 24(4), pages 585-594, February.
- Morris A. Cohen & Jehoshua Eliashberg & Teck H. Ho, 2000. "An Analysis of Several New Product Performance Metrics," Manufacturing & Service Operations Management, INFORMS, vol. 2(4), pages 337-349, July.
- Stanisława Ostasiewicz & Walenty Ostasiewicz, 2000. "Means and their applications," Annals of Operations Research, Springer, vol. 97(1), pages 337-355, December.
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- Michael Lewis & Yeujun Yoon, 2018. "An Empirical Examination of the Development and Impact of Star Power in Major League Baseball," Journal of Sports Economics, , vol. 19(2), pages 155-187, February.
- Luke Lunhua Mao & James J Zhang & Daniel P Connaughton, 2015. "Determinants of Demand for Sports Lottery: Insights from a Multilevel Model," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 5(8), pages 973-987, August.
- Stremersch, S. & Winer, R.S. & Camacho, N.M.A., 2020. "Faculty Research Incentives and Business School Health: A New Perspective from and for Marketing," ERIM Report Series Research in Management ERS-2020-013-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
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More about this item
Keywords
metrics; metric selection; metric evaluation; summary statistics; environmental effects; natural correlations; forecasting; benchmarking; monitoring; statistical biases; choosing explanatory variables;All these keywords.
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