Samohyl, Robert (2012): Audits and logistic regression, deciding what really matters in service processes: a case study of a government funding agency for research grants.
Download (1113Kb) | Preview
Governmental agencies, the back office of private firms and nongovernmental organizations experience bureaucratic processes that are often repetitive and out-of-date. These imperfections cause resource misuse and support activities that diminish to the value of the process. An important element of these bureaucratic processes is checking whether certain projects approved by the office have actually been successful in their proposed objectives. Banks and credit card companies must evaluate whether creditors have fulfilled their supposed financial worthiness, tax authorities need to classify sectors of the economy and types of tax payers for probable defaults, and research grants approved by government funding agencies should verify the use of public funds by grant recipients. In this study, logistic regression is used to estimate the probability of conformity of research grants to the financial obligations of the researcher analyzing the correlation between certain characteristics of the grant and the grant´s final status as approved or not. The logistic equation uncovers those characteristics that are most important in judging status, and supports the analysis of results as false positives and false negatives. A ROC curve is constructed which reveals not only an optimal cutoff separating conformity from nonconformity, but also discloses weak links in the chain of activities that could be easily corrected and consequently public resources preserved.
|Item Type:||MPRA Paper|
|Original Title:||Audits and logistic regression, deciding what really matters in service processes: a case study of a government funding agency for research grants|
|Keywords:||Logistic regression; ROC curve; probability; audits; government; research grants|
|Subjects:||C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C12 - Hypothesis Testing: General
M - Business Administration and Business Economics; Marketing; Accounting > M4 - Accounting and Auditing > M42 - Auditing
C - Mathematical and Quantitative Methods > C2 - Single Equation Models; Single Variables > C25 - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
|Depositing User:||Robert W. Samohyl|
|Date Deposited:||26. Sep 2012 14:25|
|Last Modified:||15. Feb 2013 10:45|
Abdi, H. (2007). Signal Detection Theory, Encyclopedia of Measurement and Statistics, 8, pp. 313–324.
Agresti A (2002). Categorical Data Analysis. JohnWiley & Sons, Hoboken, New Jersey, 2nd edition. Cecchini, M., H. Aytug, G. J. Koehler, and P. Pathak, (2010). Detecting Management Fraud in Public Companies, Management Science, vol. 56, no. 7, pp. 1146-1160, May
Cohen, J., S. Garman, and W. Gorr (2009), Empirical calibration of time series monitoring methods using receiver operating characteristic curves, International Journal of Forecasting, vol. 25, no. 3, pp. 484–497.
Cramer,J. S. (2003) The origins and development of the logit model, Bliss, August. pp. 1–19
Deming, W. Edwards (1990). Qualidade: A revolução da Administração. Rio de Janeiro, Editora Marques-Saraiva.
Dionne, G., F. Giuliano, and P. Picard (2008), Optimal Auditing with Scoring: Theory and Application to Insurance Fraud, Management Science, vol. 55, no. 1, pp. 58-70.
Dodge, H. F. (1928) A method of rating a manufactured product. Bell System Technical Journal, 7, 350–368.
Fugee, Tsung, Y. Li, and M. Jin, (2008) Statistical process control for multistage manufacturing and service operations : a review and some extensions, Int. J. Services Operations and Informatics.
Gelman, Andrew, James S. Liebman, Valerie West, and Alexander Kiss (2004) A Broken System: The Persistent Patterns of Reversals of Death Sentences in the United States, Journal of Empirical Legal Studies, Volume 1, Issue 2, 209–261, July
George, M. L (2003). Lean Six Sigma for Service. New York: McGraw-Hill
Gorr, Wilpen L. (no date) Forecasting Exceptional Demand Based on Receiver Operating Characteristics ROC. working paper, pp. 1–15.
Hastie, T., R. Tibshirani, AND J. Friedman, (2008) The Elements of Statistical Learning. Stanford, California
Hawkins, D.M. E Olwell, D.H. (1998). Cumulative sum charts and charting for quality improvement. Sringer, New York.
Komori, O. (2009) A boosting method for maximization of the area under the ROC curve, Annals of the Institute of Statistical Mathematics, vol. 63, no. 5, pp. 961-979, Oct.
Kumar, R. and A. Indrayan, (2011) Receiver operating characteristic (ROC) curve for medical researchers., Indian Pediatrics, vol. 48, no. 4, pp. 277–287.
Nembhard, D. A. and Harriet Black, A demerits control chart for autocorrelated measurements, Quality Engineering, 13(2), 179-190 (2000-01)
Ord, J. Keith The illusion of predictability: (2012). A call to action. International Journal of Forecasting, Volume 28, Issue 3, July–September, Pages 717-718
Paladini, E. P. (2000). Gestão da Qualidade. São Paulo: Átlas.
R Development Core Team (2012) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. URL http://www.r-project.org/.
R Graph Gallery (2012), http://addictedtor.free.fr/graphiques/RGraphGallery.php?graph=99, visited 8-28-12.
Reed, L. J. and J. Berkson (1929). The application of the logistic function to experimental data. Journal of Physical Chemistry 33, 760-779.
Regnier, E. Public Evacuation Decisions and Hurricane Track Uncertainty, Management Science, vol. 54, no. 1, pp. 16–28, Jan. 2008.
Samohyl, R.W. (2009). Controle Estatístico de Qualidade. Rio de Janeiro: Elsevier.
Shewhart, W. (1931). Economic control of quality of manufactured product. New York: D. Van Nostrand Company.