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Logistic Regression Table Odds 95% CI Predictor Coef SE Coef Z P Ratio Lower Upper Constant -11.5508 0.282276 -40.92 0.000 age 0.235060 0.0115234 20.40 0.000 1.26 1.24 1.29 age-squared -0.0023038 0.0001253 -18.38 0.000 1.00 1.00 1.00 educ-squared 0.0163723 0.0004017 40.76 0.000 1.02 1.02 1.02 hours squared 0.0012647 0.0000888 14.25 0.000 1.00 1.00 1.00 hours cubed -0.0000127 0.0000010 -12.35 0.000 1.00 1.00 1.00 capnet-cat gain < $5,000 -0.189060 0.109220 -1.73 0.083 0.83 0.67 1.03 gain >= $5,000 3.46054 0.114327 30.27 0.000 31.83 25.44 39.83 loss 1.15582 0.0793780 14.56 0.000 3.18 2.72 3.71 marital-status Married 2.15226 0.0749850 28.70 0.000 8.60 7.43 9.97 Never-married -0.124760 0.0931762 -1.34 0.181 0.88 0.74 1.06 Separated -0.0212868 0.175555 -0.12 0.903 0.98 0.69 1.38 Widowed 0.372877 0.169419 2.20 0.028 1.45 1.04 2.02 sex Male 0.209341 0.0554578 3.77 0.000 1.23 1.11 1.37 Log-Likelihood = -8238.566 Test that all slopes are zero: G = 11039.923, DF = 13, P-Value = 0.000

De nition The score V is a random variable de ned by f (X; ) , ln f (X; ) = V = f (X; ) where X f (x; ). The mean value of the score is EV = = = f (x; ) f (x; ) dx f (x; ) f (x; ) dx (11.265) (11.266) (11.267) (11.268) (11.269) (11.264)

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[Dewitt86] DeWitt, D, Gerber, R, Graefe, G, Heytens, M, Kumar, K, and Muralikrishna, M, Gamma A High Performance Data ow Database Machine, in Proceedings of the International Conference on Very Large Data Bases, VLDB Endowment, 1986 Reprinted, Readings in Database Systems, Morgan Kaufmann, San Francisco, CA, 1988 [Dewitt88] DeWitt, D, Ghandeharizadeh, S, and Schneider, D, A Performance Analysis of the GAMMA Database Machine, in Proceedings of ACM SIGMOD Conference, ACM, New York, 1988 [Dewitt90a] DeWitt, D, Ghandeharizadeh, S, Schneider, D, Bricker, A, Hsaio, H, and Rasmussen, R, The Gamma Database Machine Project, IEEE Transactions on Knowledge Data Engineering, Vol 2, No 1, pp 44 62, March 1990 [DeWitt90b] DeWitt, D, Futtersack, P, Maier, D, and V elez, F.

Z - I ) ovec ( Y ) 0 x'

17.3.3.2 17.3.3.2.1

, A Study of Three Alternative Workstation Server Architectures for Object-Oriented Database Systems, in Proceedings of the 16th Conference on Very Large Data Bases, pp 107 121, Brisbane, Australia, 1990 [Franklin93] Franklin, M, Carey, M, Livny, M, Local Disk Caching for Client-Server Database Systems, in Proceedings of the 19th International Conference on Very Large Data Bases (VLDB ), pp 641 655, Dublin, Ireland, August 1993 [Graefe87] Graefe, G, and Dewitt, D, The EXODUS Optimizer Generator, in Proceedings of the ACM SIGMOD Conference on Management of Data, pp 160 172, San Francisco, CA, May 1987 [Graefe93] Graefe, G, Query Evaluation Techniques for Large Databases, ACM Computing Surveys, Vol 25, No 2, pp 73 170, June 1993 [Haas97] Haas, L, Kossmann, D, Wimmers, E, and Yang, J.

specifies whether the scroll bar is horizontal (true) or vertical (false).

As with all defaults commands, the preference setting is case-sensitive, so be sure you type DoubleBoth with uppercase D and B. When you type the command, nothing happens right away. But the next time you launch an application, its windows will feature a pair of scroll arrows at each end. If you re ever nostalgic for the old way, you can use the Appearance panel in System Preferences to change it back.

As explained earlier, applying horizontal fragmentation to a table creates a set of fragments that contain disjoint rows of the table (horizontal disjointness). Horizontal fragmentation is useful because it can group together the rows of a table that satisfy the

, Optimizing Queries Across Diverse Data Sources, in Proceedings of the Conference on Very Large Data Bases (VLDB ), pp 276 285, Athens, Greece, August 1997 [Hevner79] Hevner, A, and Yao, S, Query Processing in Distributed Database Systems, IEEE Transactions on Software Engineering, Vol 5, No 3, pp 177 182, March 1979 [Ioannidis96] Ioannidis, Y, Query Optimization, ACM Computing Surveys, Vol 28, No 1, pp 121 123, March 1996 [Jarke84] Jarke,M, and Koch,J, Query Optimization in Database Systems, ACM Computing Surveys, Vol 16, No 2, pp 111 152, June 1984 [Kossmann00a] Kossmann, D, and Stocker, K, Iterative Dynamic Programming: A New Class of Query Optimization Algorithms, ACM Transactions on Database Systems, Vol 25, No 1, March 2000 [Kossmann00b] Kossmann, D, The State of Art in Distributed Query Processing, ACM Computing Surveys, Vol 32, No 4, pp 422 469, December 2000.

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