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Three observations are agged by Minitab as having high leverage: r Observation 2: All-Bran Extra Fiber (with 14 grams of ber) r Observation 27: All-Bran (with 9 grams of ber) r Observation 32: 100% Bran (with 10 grams of ber) High leverage points are de ned as data points that are extreme in the x-space. The three high-leverage points show up in Figure 3.2b as the three isolated data points on the right, since no other cereal contains more than 6 grams of ber. This extremeness with respect to ber content is enough in this instance for these observations to be labeled high leverage points. The most in uential observation is Golden Crisp, with a Cook s distance of 0.168 (not shown), which lies within the 16th percentile of the F2, 76 -distribution. Since this is less than the 20th percentile, we may conclude that there are no observations in this data set which are particularly in uential. Before we turn to inference in multiple regression, we examine the details of the multiple regression model.

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1. P. P. Angelov, Evolving Rule-Based Models: A Tool for Design of Flexible Adaptive Systems. Heidelberg, Germany: Springer-Verlag, 2002. 2. P. P. Angelov, D. P. Filev, An Approach to Online Identi cation of Takagi-Sugeno Fuzzy Models, IEEE Trans. on SMC Part B: Cybernetics, Vol. 34, No. 1, pp. 484 498, 2004. 3. R. Babuska, Fuzzy Modeling for Control. Boston: Kluwer Academic Publishers, 1998. 4. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum Press, 1981. 5. J. C. Bezdek, A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 2, pp. 1 8, 1980. 6. S. L. Chiu, Fuzzy Model Identi cation Based on Cluster Estimation, J. Intelligent and Fuzzy Systems, Vol. 2, pp. 267 278, 1994. 7. F. Crespo, R. Weber, A Methodology for Dynamic Data Mining Based on Fuzzy Clustering, Fuzzy Sets and Systems, Vol. 150 pp. 267 284, 2005. 8. R. H. Dav , Characterization and Detection of Noise in Clustering, Pattern Recognition e Letters, Vol. 12, pp. 657 664, 1991. 9. R. H. Dav , R. Krishnapuram, Robust Clustering Methods: A Uni ed View, IEEE Trans. on e Fuzzy Systems, Vol. 5 pp. 270 293, 1997. 10. M. Ester, H.-P. Kriegel, J. Sander, X. Xu, A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining, Portland, OR, 1996, pp. 226 231. 11. M. Ester, H.-P. Kriegel, J. Sander, M. Wimmer, X. Xu, Incremental Clustering for Mining in a Data Warehousing Environment. Proc. 24th Int. Conf. Very Large Databases, VLDB, 1998, pp. 323 333.

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