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Zusammenfassung

Dieses Kapitel enthält eine Schnellübersicht zu den Statistik-Befehlen unter Mathematica (Abschn. 4.1), eine Liste der package-Namen unter MS-DOS (Abschn. 4.2), Hinweise zu Literatur über Mathematica (Abschn. 4.3) und zu Statistik-Lehrbüchern (Abschn. 4.4), eine Übersicht zum Inhalt der beiliegenden CD-ROM (Abschn. 4.5), ein Quellenverzeichnis (Abschn. 4.6) und ein Stichwort-Register (Abschn. 4.7).

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Lehrbücher zu Mathematica

  1. Abell, M. L., and J. P. Braselton. 1993. Differential Equations with Mathematica. Academic Press, New York/NY (USA).zbMATHGoogle Scholar
  2. Bahder, T. B. 1995. Mathematica for scientists and engineers. Addison-Wesley Publ. Comp., Reading/MA (USA).Google Scholar
  3. Baumann, G. 1993. Mathematica in der Theoretischen Physik. Springer-Verlag, BerlinzbMATHGoogle Scholar
  4. Burkhardt, W. 1996. Erste Schritte mit Mathematica. 2., überarbeitete und erweiterte Auflage. Springer-Verlag, Berlin.CrossRefGoogle Scholar
  5. Crandall, R. E. 1991. Mathematica for the sciences. Addison-Wesley Publ. Comp., Reading/MA (USA).Google Scholar
  6. Davis, B., H. Porta, and J. Uhl. 1994. Calculus & Mathematica. Welcome to Calculus & Mathematica. Addison-Wesley Publ. Inc., Reading/MA (USA).Google Scholar
  7. Davis, B., H. Porta, and J. Uhl. 1994. Calculus & Mathematica. Approximations: Measuring Nearness. Addison-Wesley Publ. Inc., Reading/MA (USA).Google Scholar
  8. Davis. B., H. Porta, and J. Uhl. 1994. Calculus & Mathematica. Integrals: Measuring Accumulated Growth. Addison-Wesley Publ. Inc., Reading/MA (USA).Google Scholar
  9. Davis, B., H. Porta, and J. Uhl. 1994. Calculus & Mathematica. Vector Calculus: Measuring in Two and Three Dimensions. Addison-Wesley Publ. Inc., Reading/MA (USA)Google Scholar
  10. Davis, B., H. Porta, and J. Uhl. 1994. Calculus & Mathematica. Deriveatives: Measuring Growth. Addison-Wesley Publ. Inc., Reading/MA (USA)Google Scholar
  11. FEAGAN, J. 1994. Quantum methods with Mathematica. TELOS, Santa Clara/CA (USA).CrossRefGoogle Scholar
  12. Gaylord, R. J., S. N. Kamin, and P. R. Wellin. 1996. Introduction to programming with Mathematica. 2ndEdition. TELOS, Santa Clara/CA (USA).zbMATHCrossRefGoogle Scholar
  13. Gray, A. 1994. Differentialgeometrie. Spektrum Akademischer Verlag, Heidelberg.zbMATHGoogle Scholar
  14. Gray, J. W. 1994. Mastering Mathematica. Programming Methods and Applications. Academic Press, Cambridge/NIA (USA).Google Scholar
  15. Gray, T. W. 1991. Exploring Mathematics with Mathematica. Addison-Wesley-Verlag, Redwood City/CA (USA).zbMATHGoogle Scholar
  16. Hermann, C. 1995. Mathematica — Probleme, Beispiele, Lösungen. Thomson Publ., Bonn.Google Scholar
  17. Kaufmann, S. 1992. Mathematica als Werkzeug. Eine Einführung mit Anwendungsbeispielen. Birkhäuser Verlag, Basel.CrossRefGoogle Scholar
  18. Kofler, M. 1992. Mathematica — Einführung und Leitfaden. 1. Auflage. Addison-Wesley Verlag, Bonn.Google Scholar
  19. Maeder, R. E. 1991. Programming in Mathematica. 2nd Edition. Addison-Wesley Publ. Comp., Redwood City/CA (USA).Google Scholar
  20. Maeder, R. E. 1993. Informatik für Mathematiker und Naturwissenschaftler. Addison-Wesley Verlag, Bonn.zbMATHGoogle Scholar
  21. Pidgeon, C. (Ed.). 1996. Tutorials for the Biomedical Sciences. Verlag Chemie, New York/NY (USA).Google Scholar
  22. Riddle, A. 1995. Applied electronics with Mathematica. Addison-Wesley Publ. Comp., Reading/MA (USA).Google Scholar
  23. Ross, C. C. 1995. Differential Equations. An Introduction with Mathematica. Springer-Verlag, New York.Google Scholar
  24. Schaper, R. 1994. Grafik mit Mathematica. Von den For-meln zu den Formen. Addison-Wesley Verlag, Bonn.Google Scholar
  25. SKEEL, R., and J. B. Keiper. 1993. Elementary numerical computing with NIathematica. McGraw-Hill, Inc., New York/NY (USA).Google Scholar
  26. Smith, C., and N. Blachman. 1995. Mathematica Graphics Guidebook. 2nd Edition. Addison-Wesley Publ. Inc., Reading/MA (USA).Google Scholar
  27. Stelzer, E. H. K. 1993. Mathematica. Ein systematisches Lehrbuch mit Anwendungs-Beispielen. Addison-Wesley Verlag, Bonn.Google Scholar
  28. Varian, H. R. (Ed.). 1993. Economic and financial mo- deling with Mathematica. TELOS, Santa Clara/CA (USA).Google Scholar
  29. Vvedensky, D. 1993. Partial differential equations with Mathematica. Addison-Wesley Verlag, Wokingham (Engl.).zbMATHGoogle Scholar
  30. Wagon, S. 1993. NIathematica in Aktion. Spektrum Akademischer Verlag, Heidelberg.Google Scholar
  31. Wickham-Jones, T. 1994. Mathematica Graphics. Techniques. Applications. TELOS, Santa Clara/CA (USA).zbMATHCrossRefGoogle Scholar
  32. Wolfram Research. 1991. matica Packages. Version 2.0. paign/IL (USA).Google Scholar
  33. Wolfram Research. 1992. matica Packages. Version 2.1. paign/IL (USA).Google Scholar
  34. Wolfram Research. 1993. matica Packages. Version 2.2. paign/IL (USA).Google Scholar
  35. Wolfram, Stephen. 1991. Mathematica—A System for Doing Mathematics. 2nd Edition. Addison-Wesley Publ. Comp. Inc., Redwood City/CA (USA).Google Scholar
  36. Wolfram, Stephen. 1994. Mathematica —Ein System für Mathematik auf dem Computer. 2. Auflage. Addison-Wesley Publ. Comp., Bonn.Google Scholar
  37. Wolfram, Stephen. 1996. The Mathematica Book. 3rd Edition. Wolfram Media/Cambridge University Press, Cambridge (UK).zbMATHGoogle Scholar
  38. Zimmermann, R. L. 1995. Mathematica for physics. Addison-Wesley Publ. Comp., Reading/NIA (USA).Google Scholar

Zusammenstellung weiterer Publikationen zu Anwendungen von Mathematica

  1. Abbot, P. (Ed.). 1994. Peak Fitting. Mathematica Journal 4(4):21–22.Google Scholar
  2. Blachman, N. 1992. Nonlinear Fitting, Looping and Recursion. Mathematica Journal 2(2): 32–34.Google Scholar
  3. Dudas, M. M., and H. C. Hsieh. 1992. Application of quantum mechanical perturbation theory to molecular vibrational-rotational analysis. Mathematica Journal2 (2): 66–69.Google Scholar
  4. Farza, M., and A. Cheruy. 1994. BIOSTEM: software automatic design of estimators in bioprocess engineering. Com put. Appl. Biosci. 10: 477 - 488.Google Scholar
  5. Fultz, J. 1994. Curve Fits, Math Link for Excel, and Color. Mathematica Journal 4(4):44–53.Google Scholar
  6. Gasparovic, C., M. Cabanas, and C. Arus. 1995. A simple approach to the design of a shielded gradient probe for high-resolution in vivo spectroscopy. J. Mag. Res. B. 109: 146–152.CrossRefGoogle Scholar
  7. Gronlund, S., C. F. Sheu, and R. Ratcliff. 1990. Implementation of global memory models with software that does symbolic computation. Beh. Res. Meth. Instr. Comp. 22: 228–235.CrossRefGoogle Scholar
  8. He, X., D. N. Ku, and J. E. Moore. 1993. Simple calculation of the velocity profiles for pulsative flow in a blood vessel using Mathematica. Ann. Biomed. Eng. 21: 45–49.CrossRefGoogle Scholar
  9. Hunka, S. 1995. Identifying regions of significance in ANCOVA problems having non-homogeneous regressions. Br. J. Math. Stat. Psych. 48: 161–188.zbMATHCrossRefGoogle Scholar
  10. Kleene, S. J., and H. C. Cejtin. 1994. Solving buffering problems with Mathematica software. Anal. Biochem. 222: 310–314.CrossRefGoogle Scholar
  11. Korsan, R. J. 1993. Fractals and Time Series Analysis. Mathematics Journal 3(1): 39–44.Google Scholar
  12. Lorig, T., and T. P. Urbach. 1995. Event-related potential analysis using Mathematica. Beh. Res. Meth. Instrum. Comp. 27: 358–366.CrossRefGoogle Scholar
  13. Mader, R. E. 1992. Minimal surfaces. Mathematica Journal 2(2): 25–30.Google Scholar
  14. Martin, E. 1992. Statistics. Wolfram Research. PostScript- Zu MathSource siehe Datei auf ?MathSource. Abschn. 1.3 Google Scholar
  15. Mcalarney, M. E., G. Dasgupta, M. L. Moss, and L. Salentijn-Moss. 1992. Anatomical macroelements in the study of craniofacial rat growth. J. Craniofac. Genet. Dey. Biol. 12: 3–12.Google Scholar
  16. Simon, J. L., and P. Bruce. 1993. Probability and Statistics with Resampling Stats and Mathematica. Mathematica Journal 3(1): 48–55.Google Scholar
  17. Stine, R. A. 1995. Data analysis using Mathematica. Sociological Meth. Res. 23: 352–272.CrossRefGoogle Scholar
  18. Stoner, C. D. 1993. Quantitative determination of the steady-state kinetics of multienzyme reactions using the algebraic rate equations for the component single-enzyme reactions. Biochem. J. 219: 585–593.Google Scholar
  19. Zheng, Q. 1995. On the MVK stochastic carconogenesis model with Erlang distributed cell life lengths. Risk. Anal. 15: 495–502.CrossRefGoogle Scholar

Zusammenstellung einiger Statistik-Lehrbücher

  1. Altman, D. G. 1980/81. Statistics and ethics in medical research. Serie in Br. Med. J. 281:1182–1184.Google Scholar
  2. Altman, D. G. 1980/81. Statistics and ethics in medical research. Serie in Br. Med. J. 281: 1267–1269.Google Scholar
  3. Altman, D. G. 1980/81. Statistics and ethics in medical research. Serie in Br. Med. J. 281: 1336–1399.Google Scholar
  4. Altman, D. G. 1980/81. Statistics and ethics in medical research. Serie in Br. Med. J. 281: 1399–1401.Google Scholar
  5. Altman, D. G. 1980/81. Statistics and ethics in medical research. Serie in Br. Med. J. 281: 1473–1475.Google Scholar
  6. Altman, D. G. 1980/81. Statistics and ethics in medical research. Serie in Br. Med. J. 281: 1542–1544.Google Scholar
  7. Altman, D. G. 1980/81. Statistics and ethics in medical research. Serie in Br. Med. J. 281: 1612–1614.Google Scholar
  8. Altman, D. G. 1980/81. Statistics and ethics in medical research. Serie in Br. Med. J. 282: 44–47.Google Scholar
  9. Bohley, J 1989. Statistik. Einführendes Lehrbuch für Wirtschafts-und Sozialwissenschaftler. 3. Auflage. R. Oldenbourg Verlag, München.Google Scholar
  10. Bol, G. 1995. Deskriptive Statistik. 3. Auflage. R. Oldenbourg Verlag. München.Google Scholar
  11. Bortz, J., G. A. Lienert, und K. Boehnke. 1990. Verteilungsfreie Methoden in der Biostatistik. Springer-Verlag, Berlin.Google Scholar
  12. Brown, R. A., and J. S. Beck. 1988/89. Statistics on microcomputers: A non-algebraic guide to their appropriate use in biomedical research and pathology laboratory practice. A series of six articles. J. Clin. Pathol. 41:1033–1038.Google Scholar
  13. Brown, R. A., and J. S. Beck. 1988/89. Statistics on microcomputers: A non-algebraic guide to their appropriate use in biomedical research and pathology laboratory practice. A series of six articles. J. Clin. Pathol. 41: 1148–1154.Google Scholar
  14. Brown, R. A., and J. S. Beck. 1988/89. Statistics on microcomputers: A non-algebraic guide to their appropriate use in biomedical research and pathology laboratory practice. A series of six articles. J. Clin. Pathol. 41:1256–1262.Google Scholar
  15. Brown, R. A., and J. S. Beck. 1988/89. Statistics on microcomputers: A non-algebraic guide to their appropriate use in biomedical research and pathology laboratory practice. A series of six articles. J. Clin. Pathol. 42:4–12.Google Scholar
  16. Brown, R. A., and J. S. Beck. 1988/89. Statistics on microcomputers: A non-algebraic guide to their appropriate use in biomedical research and pathology laboratory practice. A series of six articles. J. Clin. Pathol. 42:117–122.Google Scholar
  17. Brown, R. A., and J. S. Beck. 1988/89. Statistics on microcomputers: A non-algebraic guide to their appropriate use in biomedical research and pathology laboratory practice. A series of six articles. J. Clin. Pathol. 42: 225–230.Google Scholar
  18. Chambers, J. M., W. S. Cleveland, B. Kleiner, and P. A. Tukey. 1983. Graphical methods for data analysis. Wads-worth & Brooks/Cole Publ. Co., Pacific Grove/CA (USA).zbMATHGoogle Scholar
  19. Diehl, J. M., und H. U. Kohr. 1994. Deskriptive Statistik. 11. Auflage. Verlag Dietmar Klotz, Eschborn bei Frankfurt/Main.Google Scholar
  20. Diehl, J. M., und R. Arbinger. 1992. Einführung in die Inferfenzstatistik. 2. Auflage. Verlag Dietmar Klotz, Eschborn bei Frankfurt/Main.Google Scholar
  21. Feinstein, A. R. 1970–1981. Clinical biostatistics. Clin. Pharmacol. Ther. 11–29.Google Scholar
  22. Gessler, J. R. 1993. Statistische Graphik. Birkhäuser Verlag, Basel.zbMATHGoogle Scholar
  23. Leiner, 1991. Einführung in die Zeitreihen-Analyse. 3. Auflage. R. Oldenbourg-Verlag, München.Google Scholar
  24. Lienert, G. A. 1986. Verteilungsfreie Methoden in der Biostatistik. Band I, 3. Auflage. Verlag Anton Hain, Meisenheim/Taunus.Google Scholar
  25. Lienert, G. A. 1978. Verteilungsfreie Methoden in der Biostatistik. Band II, 2. Auflage. Verlag Anton Hain, Meisenheim/Taunus.Google Scholar
  26. Lienert, G. A. 1976. Verteilungsfreie Methoden in der Biostatistik. Tafelband. Verlag Anton Hain, Meisenheim/Taunus.Google Scholar
  27. Mainland, D. 1966–1968. Statistical ward rounds. Clin. Pharmacol. Ther. 8–10.Google Scholar
  28. Precht, M., und R. Kraft. 1993. Biostatistik 2. R. Oldenbourg Verlag. München.Google Scholar
  29. Puhani, J. 1994. Kleine Formelsammlung zur Statistik. Bayerische Verlagsanstalt Bamberg.Google Scholar
  30. Rasch, D. 1976. Einführung in die mathematische Statistik. II. Anwendungen. VEB Deutscher Verlag der Wissenschaften, Berlin.zbMATHGoogle Scholar
  31. Ratkowsky, D. A. 1983. Nonlinear Regression modelling. Marcel Dekker, Inc., New York.Google Scholar
  32. Rinne, H. 1984. Statistische Formelsammlung. Verlag Harri Deutsch, Thun.Google Scholar
  33. Röntz, B., und H. G. Strohe (Hrsg.). 1994. Lexikon Statistik. Gabler Verlag, Wiesbaden.Google Scholar
  34. Sachs, L. 1993. Statistische Methoden. Planung und Auswertung. 7. Auflage. Springer-Verlag, Berlin.Google Scholar
  35. Sachs. L. 1990. Statistische Methoden 2. Planung und Auswertung. 7. Auflage. Springer-Verlag, Berlin.Google Scholar
  36. Schlittgen, R., und B. H. J. Streitberg. 1995. Zeitreihen-analyse. R. Oldenbourg-Verlag, München.Google Scholar
  37. Snedecor, G. W., and W. G. Cochran. 1982. Statistical Methods. 7th Edition. Iowa State University Press, Ames/ Iowa (USA )Google Scholar
  38. Stahel, W. A. 1995. Statistische Datenanalyse. Eine Einführung für Naturwissenschaftler. Vieweg Verlags-GmbH, Braunschweig.Google Scholar
  39. Taylor, J. R. 1988. Fehleranalyse. Eine Einführung in die Untersuchung von Unsicherheiten in physikalischen Messungen. Verlag Chemie, Weinheim.Google Scholar
  40. Zar, J. H. 1984. Biostatistical Analysis. 2nd Edition. Prentice-Hall, Englewood Cliffs/NJ (USA).Google Scholar

Quellen

  1. Ariens, E. M., J. M. van Rossum, and A. M. Simonis. 1956. A theoretical basis of molecular pharmacology. Part I: Interactions of one or two compounds with one ore two receptors. Arzneimittel-Forsch. 6: 282–293.Google Scholar
  2. Ariens, E. M., J. M. van Rossum, and A. M. Simonis. 1957. Affinity, intrinsic activity and drug interaction. Pharmacol. Rev. 9: 218–236.Google Scholar
  3. Barnett, V. I. C. (Ed.). 1981. Interpreting Multivariate Data. John Wiley & Sons, Chichester (UK).zbMATHGoogle Scholar
  4. Barnett, V. I. C., and T. Lewis. 1994. Outliers in Statistical Data. 3rd Edition. John Wiley & Sons, Chichester (UK).zbMATHGoogle Scholar
  5. Bartlett, M. S., 1937. Properties of sufficiency and statistical tests. Proc. Royal Soc. (A) 160: 268–282.CrossRefGoogle Scholar
  6. Blomqvist, N. 1950. On a measure of dependence between two random variables. Ann. Math. Statistics 21: 593–601.MathSciNetzbMATHCrossRefGoogle Scholar
  7. Bühm, W. G., and J. Kahmann. 1984. A survey on curve and surface methods in CAGD. Computer Aided Geometric Design 1:1–60.CrossRefGoogle Scholar
  8. Bol, G. 1995. Deskriptive Statistik. 3. Auflage. R. Oldenbourg-Verlag, München.Google Scholar
  9. Bortz, J., G. A. Linert und K. Boehnke. 1990. Verteilungsfreie Methoden in der Biostatistik. Springer-Verlag, Berlin.Google Scholar
  10. Chambers, J. M., W. S. Cleveland, B. Kleiner, and P. A. Tukey. 1983. Graphical Methods for Data Analysis. Wadsworth & Brooks/Cole Publ. Company, Pacific Grove.zbMATHGoogle Scholar
  11. Chauvenet, W. 1876. Manual of Spherical and Practical Astronomy. Philadelphia.Google Scholar
  12. Cochran, W. G. 1941. The distribution of the largest of a set of estimated variances as a fraction of their total. Ann. Eugen. (London) 11: 47–61.MathSciNetCrossRefGoogle Scholar
  13. Cox, D. R., and A. Stuart. 1955. Quick sign tests for trend in location and dispersion. Biometrika 43: 423 - 435.MathSciNetGoogle Scholar
  14. Damper, R. I. 1995. Introduction to discrete-time signals and systems. Chapman & Hall, London.Google Scholar
  15. Ernst, H. 1991. Einführung in die digitale Bildanalyse. Franzis-Verlag, München.Google Scholar
  16. Fergusion, T. S. 1961. Rules for rejection of outliers. Revue Inst. Int. Stat. 3: 29–43.CrossRefGoogle Scholar
  17. Finney, D. J. 1971. Probit Analysis. 3rd Edition. Cambridge University Press, Cambridge.Google Scholar
  18. Fisher, R. A. 1958. Statistical Methods for Research Workers. 13th Edition. Hafner, New York.Google Scholar
  19. Gaylord, R. J., S. N. Kamin, and P. R Wellin. 1996. An Introduction to Programming with Mathematica. 2nd Edition. TELOS, Santa Clara/CA (USA).zbMATHCrossRefGoogle Scholar
  20. Gessler, J. R. 1993, Statistische Graphik. Birkhäuser Verlag, Basel.zbMATHGoogle Scholar
  21. Gosset, W. S. 1908. The probable error of mean. Biometrika 6: 1–25.Google Scholar
  22. Grubbs, F. E. 1969. Procedures for detecting outlying observations in samples. Technometrics 11: 1–21.CrossRefGoogle Scholar
  23. Hartley, H.O. 1950. The maximum F-ratio as a short-cut test for heterogeneity of variance. Biometrika 37: 308–312.MathSciNetzbMATHGoogle Scholar
  24. Hazen, A. 1914. Storage to be provided in impounding reservoirs for municipal water supply. Am. Soc. Civil Eng. 77: 1539–1669.Google Scholar
  25. Hill, A. V. 1910. The possible effects of the aggregation of the molecules of hwmoglobin on its dissociation curves. J. Physiol. 40: 4–7.Google Scholar
  26. Hochstädter, D., und U. Kaiser. 1988. Varianz-und Kovarianzanalyse. Verlag Harri Deutsch, Frankfurt/Main.Google Scholar
  27. Jäger, A. H., U. Bogdahn, B. Pfeufer, J. Richter, R. Apfel and A. Dekant. 1993. In vitrostudies on interaction of 4hydroperoxy-ifosfamide and radiotherapy in malignant gliomas. Anticancer Res. 13: 2221–2228.Google Scholar
  28. Kellerer, A. M., and H. H. Rossi. 1972. The theory of dual radiation action. Current Topics Rad. Res. Quart. 8: 85–158.Google Scholar
  29. Keuls, M. 1952. The use of „studentized range“ in connection with an analysis of variance. Euphytica 1: 112–122.CrossRefGoogle Scholar
  30. Khalfina, N. M. 1986. Detection of outliers in results of observations by means of the Chauvenets test. Zapiski Nauch-nykh Seminarov Leningradskogo Otdeleniya Matematicheskogo-Instituta imeni V. A. Steklova Akademii Nauk SSSR 153: 153–159zbMATHGoogle Scholar
  31. Khalfina, N. M. 1986. Detection of outliers in results of observations by means of the Chauvenets test. Zapiski Nauch-nykh Seminarov Leningradskogo Otdeleniya Matematicheskogo, Instituta imeni V. A. Steklova Akademii Nauk SSSR 176; 180–181.Google Scholar
  32. Khalfina, N. M. 1989. Detection of outliers by Chauvenets method in observations connected in a homogeneous Markov chain. Zapiski Nauchnykh Seminarov Leningradskogo Ot-deleniya Matematicheskogo, Instituta imeni V. A. Steklova Akademii Nauk SSSR 177: 163–169; 192.Google Scholar
  33. Kruskal, W. H. 1952. A nonparametric test for the several sampling problem. Ann. Math. Statistics 23: 525–540.MathSciNetzbMATHCrossRefGoogle Scholar
  34. Kruskal, W. H., and W. A. Wallis. 1952. Use of ranks in one-criterion variance analysis. J. Amer. Stat. Assoc. 47: 583–621.zbMATHCrossRefGoogle Scholar
  35. Kruskal, W. H., and W. A. Wallis. 1953. Use of ranks in one-criterion variance analysis. J. Amer. Stat. Assoc. 48: 907–911.zbMATHCrossRefGoogle Scholar
  36. Larimore, W. E., and R. K. Mehra. 1985. The problem of overfitting data. BYTE 10/85:167–180.Google Scholar
  37. Legendre, A. M. 1805. Nouvelles méthodes pour la détermination des orbites des comètes. Firmin Ditot, Paris.Google Scholar
  38. Leiner, B. 1991. Einführung in die Zeitreihen-Analyse. 3. Auflage. R. Oldenbourg-Verlag. München.Google Scholar
  39. Levenberg, K. 1944. A method for the solution of certain non-linear problems in least squares. Quart. Appl. Math 2: 164–168.MathSciNetzbMATHGoogle Scholar
  40. Lienert, G. A. 1978. Verteilungsfreie Methoden in der Bio- statistik. Band 2. Verlag Anton Hain, Meisenheim/Taunus.Google Scholar
  41. Lienert, G. A. 1986. Verteilungsfreie Methoden in der Bio- statistik. Band 1. Verlag Anton Hain, Meisenheim/Taunus.Google Scholar
  42. Lineweaver, H., and D. Burk. 1934. The determination of enzyme dissociation constants. J. Amer. Chem. Soc 56: 658–666.CrossRefGoogle Scholar
  43. Lorenz, M. O. 1905. Methods of measuring the concentration of wealth. In: Publications of the American Statistical Association 70.Google Scholar
  44. Madow, W. G. 1940. Notes on tests of departure from normality. J. Amer. Stat. Assoc. 35: 515 - 517.MathSciNetzbMATHCrossRefGoogle Scholar
  45. Maeder, R. E. 1993. Informatik für Mathematiker und Naturwissenschaftler. Addison-Wesley Verlag, Bonn.zbMATHGoogle Scholar
  46. Mann, H. B., and D. R. und Whitney. 1947. On a test of whether one of two random variables ist stochastically larger than the other. Ann. Math. Stat. 18: 50 - 60.MathSciNetzbMATHCrossRefGoogle Scholar
  47. Marquardt, D. W. 1963. An algorithm for least squares estimation of nonlinear parameters. J. Soc. Industr. Appl. Math. 2: 431–441.MathSciNetCrossRefGoogle Scholar
  48. Mason, A. L., and C. B. und Bell. 1986. New Lillefors and Srinivasan tables with applications. Comm. Stat. Sim. Comp. 15: 451–477.MathSciNetzbMATHCrossRefGoogle Scholar
  49. Morgan, B. J. T. 1992. Analysis of Quantal Response Data. Chapman & Hall, London.Google Scholar
  50. Newman, D. 1942. The distribution of the range in samples from a normal population. Biometrika 32: 301–310.MathSciNetGoogle Scholar
  51. Pederson, B. M. 1988. Graphis Diagram 1. Graphis Press Corp., Zürich.Google Scholar
  52. Pagurova, V. I., 1985. The Chauvenet test for detecting several outliers. Akademiya Nauk SSSR. Teoriya Veroyatnostei i ee Primeneniya30: 558–561.MathSciNetGoogle Scholar
  53. Pfeufer, B. 1993. Interaktionen in der Kombination von Chemo-und Strahlen-Therapie maligner Hirntumoren. Dissertation, Bayerische Julius-Maximilians-Universität, Würzburg.Google Scholar
  54. Pöch, G. 1993. Combined Effects of Drug and Toxic Agents. Springer-Verlag, Wien.CrossRefGoogle Scholar
  55. Press, W. H., P. Flannery, S. A. Teukolsky, and W. T. Vetterling. 1989. Numerical Recipes in Pascal. Cambridge University Press, Cambridge.zbMATHGoogle Scholar
  56. Prunty, L. 1983. Curve fitting with smooth functions that are piecewise-linear in the limit. Biometrics 39: 857–866.CrossRefGoogle Scholar
  57. Quenouille, M. H. 1959. Rapid Statistical Calculations. Griffin, London.zbMATHGoogle Scholar
  58. Ratkowsky, D. A. 1983. Nonlinear Regression Modeling. A Unified Practical Approach. Marcel Dekker, New York.Google Scholar
  59. Rice, J. R. 1964. The Approximation of Functions. Addison- Wesley Publ. Comp., Reading/MA (USA).zbMATHGoogle Scholar
  60. Riedwyl, H. 1987. Graphische Gestaltung von Zahlenma-terial. Verlag Paul Haupt, Bern.Google Scholar
  61. Sachs, L. 1992. Angewandte Statistik. Springer-Verlag, Berlin.zbMATHCrossRefGoogle Scholar
  62. Scheffé, H. 1959. The Analysis of Variance. John Wiley & Sons, New York/NY (USA).zbMATHGoogle Scholar
  63. Schlittgen, R., und B. H. J. Streitberg. 1995. Zeitreihen-analyse. R. Oldenbourg-Verlag, München.Google Scholar
  64. Snedecor, G. W., and W. C. Cochran. 1982. Statistical Methods. 7thEdition. The Iowa State University Press, Ames/IO (USA).Google Scholar
  65. Taylor, J. R. 1988. Fehleranalyse. Verlag Chemie, Weinheim.Google Scholar
  66. Tufte, E. R. 1982. The Visual Display of Quantitative Information. Graphics Press, London.Google Scholar
  67. Tukey, J. W. 1949. Comparing individual means in the analysis of variance. Biometrics 5: 99–114.MathSciNetCrossRefGoogle Scholar
  68. Victor, N. 1978. Alternativen zum klassischen Histogramm. Meth. Inform. Med. 17: 120–126.Google Scholar
  69. Vollmar, H. J. (Hrsg.). 1985. Biometrie in der chemisch-pharmazeutischen Industrie 2. Biometrische Analyse von Kombinationswirkungen. Gustav-Fischer Verlag, Stuttgart.Google Scholar
  70. Vollmar, H. J. (Hrsg.). 1986. Biometrie in der chemisch-pharmazeutischen Industrie 3. Auswertungs-und Darstellungsmethoden — Risikoextrapolation zur Kanzerogenität. Gustav-Fischer Verlag, Stuttgart.Google Scholar
  71. Weber, M. 1989. Turbo Pascal Tools. 2. Auflage. Friedr.Vieweg Verlag, Braunschweig.Google Scholar
  72. Welch, B. L. 1951. On the comparison of several mean values: an alternative approach. Biometrika 38: 330–336.MathSciNetzbMATHGoogle Scholar
  73. Whittemore, A. S., and J. B. Keller. 1986. Survival estimation using splines. Biometrics 42: 495–506.MathSciNetzbMATHCrossRefGoogle Scholar
  74. Wilcoxon, F. 1945. Individual comparisons by ranking methods. Biometrics 1: 80–83.CrossRefGoogle Scholar
  75. Williams, E. J. 1959. Regression Analysis. John Wiley & Sons, New York.zbMATHGoogle Scholar
  76. Wolfram Research. 1992. Guide to Standard Mathematica Packages. Technical Report. Version 2.1. Champaign/IL (USA).Google Scholar
  77. Wolfram Research. 1993. Guide to Standard Mathematica Packages. Technical Report. Version 2.2. Champaign/IL (USA).Google Scholar
  78. Wolfram, Stephen. 1991. MathematicaA System for Doing Mathematics. 2nd Edition. Addison-Wesley Publ. Comp. Inc., Redwood City/CA (USA).Google Scholar
  79. Yates, F. 1934. Contingency tables involving small numbers and the X 2 test. J. Roy. Stat. Soc. Suppl. 1: 217–235.zbMATHCrossRefGoogle Scholar
  80. Zar, J. H. 1984. Biostatistical Analysis. 2nd Edition. Prentice-Hall Intern., Englewood CliffSs/NJ (USA).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Andreas H. Jäger
    • 1
  1. 1.BurggrumbachGermany

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