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Logistic regression is a popular method for building predictive models when the response variable is binary. Many data mining problems fall into this category. For example, a patient contracts or does not contract a disease or a cell phone subscriber does or does not switch services. Logistic regression models are built from one or more independent variables, that can be continuous, discrete, or a mixture of both. In addition to classifying observations into these categories, logistic regression will also calculate a probability that re ects the likelihood of a positive outcome. This is especially useful in prioritizing the results. As an example, a marketing company may use logistic regression to predict whether a customer will or will not buy a speci c new product. While the model may predict more customers than the company has resources to pursue, the computed probability can be used to prioritize the most promising candidates. Unlike discriminant analysis, logistic regression does not assume that the independent variables are normally distributed, or have similar variance in each group. There are, however, a number of limitations that apply to logistic regression including (1) a requirement for a large data set with suf cient examples of both categories, (2) that the independent variables are neither additive nor collinear, and (3) that outliers can be problematic.



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Shepherd GG, Welsh HL (1956) Can J Phys 34:1425 Felker PM, Henson BF, Venturo VA, Hartland GV (1992) In: Proc XllIth Int Conf Raman Spectrosc, Kiefer W, Cardona M, Schaack G, Schneider FW, Schrotter HW (eds) Wiley, Chichester, p 230 Fellgett P (1958) J Phys Radium (Paris) 19:1X7 Felsenfcld G (1978) Nature 271:llS Ferini E (1931) Z Phys 71:250 Fernander-Sanchez JM, Montero S (1989) J Chem Phys 902909 Feriiandez-Sanchez JM, Montero S (1991) J Chem Phys 94:7788 FernandeAanchez JM, Valdenebro AG, Montero S (1989) J Chem Phys 91:3327 Ferraro JR, Basile LJ (1974) Appl Spectrosc 28:505 Ferraro JR, Basilc LJ ( 1980) Appl Spectrosc 34:216 Ferraro JR, Nakamoto K ( 1 994) Introductory Raman Spectroscopy, Academic Press, New York Ferraro JR, Rein AJ (1985) Applications of Diffuse Reflectance Spectroscopy in the Far-Infrared Region In: Ferraro JR, Basile LJ (eds) Fourier Transform Infrared Spectroscopy, vol 4.





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Product Category URL Supported Platforms License Features JUnit Java unit testing tool http://www.junit.org/ Java 1.2, 1.3, 1.4 Common public license Supported in Eclipse, NetBeans, and Ant

Acadetnic, Orlando Ferraro JR, Ziomek JS (1975) Introductory Group Theory Plenum Press, New York Fiber Systems (1991) Product Information Silver Springs, USA Fickenscher M, Laubereau A (1990) J Raman Spectrosc 21:857 Fickenscher M, Purucker HG, Laubereau A (1992) Chem Phys Lett 191:182 Fillaux F, Baron MH (1981) Chem Phys 6 2 2 7 5 Finch A, Gates PN, Radcliffe K, Dickson FN, Bentley FF ( I 970) Chemical Applications of Far Infrared Spectroscopy.

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Academic Press, London Finch JT, Lutter LC, Rhodes D, Brown RS, Rushton 8,Levitt M, Klug A (1977) Nature 269:29 Fincher jr CR, Chen CE, Heeger AJ, Macdiarmid AG, Hastings JB (1982) Phys Rev Lett 48:100 Finkelmann H, Pohl L, Rubner R, Sackmann H, Stegemeyer H (eds) (1993) Berichtsheft Hauptversammlung 1993 Neue Eigenschaften und Anwendungen von Flussigkristallen, Ber Bunsenges Phys Chem 97: I169 Finkelmann H, Stegemeyer H ( I 974) Ber Bunsenges Physik Chem 78:869 Finsterholzl H (1982) Ber Bunsenges Phys Chem 86:797 Finsterholzl H, Hochenbleicher JG, Strey G (1977) J Raman Spectrosc 6:13 Finsterholzl H, Kbckner HW, Srinivasan K, Schrotter HW, Brandmiiller J (1978) Indian J Pure Appl Phys 16:370 Finsterholzl H, Schrotter HW, Strey G (1981) J Raman Spectrosc 11375 Firth S, Klotzbuecher WE, Poliakoff M, Turner JJ (1987) Inorg Chem 26:3370 Fischer JE, Cox DE (1992) J Phys Chem Solids 53: Fischer M, Schmid R (1985) Chemie und Physik.

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