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Type-1 asymptotic maximum-value distribution is suggested 0.025, u 46.92 G amma is suggested, (ii)  0:317 Poisson is suggested, (ii) v 45:81 N ormal is suggested, (ii) m 2860,  202:9 Poisson is suggested, (ii) v 7:0 Lognormal is suggested, (ii) X 76:2, 2 X 0:203 ln
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ve-number-summary for the churn = true customers indicates higher day minute usage than for the churn = false customers, supporting the observation from Figure 4.2. Is this difference signi cant A two-sample t-test is carried out, the null hypothesis being that there is no difference in true mean day minute usage between churners and nonchurners. The results are shown in Table 4.9. The resulting t-statistic is 9.68, with a p-value rounding to zero, representing strong signi cance. That is, the null hypothesis that there is no difference in true mean day minute usage between churners and nonchurners is strongly rejected. A word of caution is in order here about carrying out inference in data mining problems, or indeed in any problem where the sample size is very large. Most statistical tests become very sensitive at very large sample sizes, rejecting the null hypothesis for tiny effects. The analyst needs to understand that just because the effect is found to be statistically signi cant because of the huge sample size, it doesn t necessarily follow that the effect is of practical signi cance. The analyst should keep in mind the constraints and desiderata of the business or research problem, seek con uence of results from a variety of models, and always retain a clear eye for the interpretability of the model and the applicability of the model to the original problem. Note that the t-test does not give us an idea of how an increase in day minutes affects the odds that a customer will churn. Neither does the t-test provide a method for nding the probability that a particular customer will churn, based on the customer s day minutes usage. To learn this, we must turn to logistic regression, which we now carry out, with the results given in Table 4.10. First, we verify the relationship between the odds ratio for day minutes and its coef cient. OR = eb1 = e0.0112717 = 1.011335 1.01, as shown in Table 4.10. We = discuss interpreting this value a bit later. In this example we have b0 = 3.92929 and b1 = 0.0112717. Thus, the probability of churning (x) = e 0 + 1 x /(1 + e 0 + 1 x ) for
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T A B L E 15.3. Recognition Rates, Method 3
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We summarize brie y the preliminary NORM analyses with this new data set. For these analyses, there were 28 variables in total. There were 105 patterns of missing and nonmissing values, 56 of which involved just a single individual. The largest pattern (N 218) was the pattern of complete data. For this data set, EM converged normally in 42 iterations. Once you have run EM within NORM, the next step for multiple imputation is data augmentation. Click on the Data augmentation tab. The Series Button. The information here is for setting up the diagnostics for data augmentation. In order to run the diagnostics, you should click on one of the Save options. Click on Save all parameters to save information about all of the parameters (variances, covariances, and means). If the number of variables is small, or if you have little experience with a particular data set, this may be a good option. However, with this option, the program saves all parameter estimates at every step of data augmentation. Thus, with a large number of variables and a large number of steps, the le containing this information could be huge (e.g., 50 to 100 MB or larger). Thus, a good compromise is to click on Save only worst linear function. If the results for the worst linear function are acceptable, then the results for all other parameter estimates will be no worse than this. This le is generally very small. The Imputation Button. Usually you will want to click on Impute at every kth iteration. However, what value should be used for k We noted earlier that one of the key questions when doing data augmentation is how many steps are required before two imputed data sets are like two random draws from a population. There are two approaches to be taken here: (a) One can select a conservative number of steps between imputed data sets, or (b) one can perform the diagnostics to see how many steps between imputed data sets are suggested by the data. We recommend a combination of these two approaches. With other implementations of multiple imputation, the entire process may be more automated than the process described for NORM. For example, in SAS 8.2 one runs EM, MCMC, and imputation all in a single step without user input. This reduction of steps, however, is only apparent. Regardless of what software one uses, the user must, as we describe below, make decisions along the way. First, determine how many iterations it took EM to converge. We recommend that you begin with this number for k. For example, it took EM 42 iterations to converge in our example. Thus, to be somewhat conservative, we began by setting k to 50. That means that NORM will produce an imputed
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Whitt, M. C., 493 Wichstrom, L., 868 Wickens, C. D., 247, 248, 250, 251 Wicker, A. W., 538 Wickwire, T. L., 126 Widaman, K. F., 776, 781, 788 Widmeyer, W. N., 142, 270, 272, 800, 801, 802, 803, 804, 805, 807, 808, 810, 815 Wiechman, S. A., 317, 322 Wiegand, S. J., 479 Wienbruch, C., 96 Wiener, N., 246 Wierenga, S. A., 166, 169, 251, 252, 434 Wiersma, L. D., 191 Wierwille, W. W., 248, 249, 273 Wiese, D. M., 410 Wiese-Bjornstal, D. M., 405, 406, 407, 408, 410, 411, 413, 416, 417, 418 Wigfield, A., 22, 72, 647, 686, 691, 694, 695, 702 Wightman, D. C., 185 Wilber, K., 613 Wilckens, J. H., 414 Wilcox, A., 586, 588 Wilcox, H. C., 316 Wilcox, S., 647, 655 Wilf ley, D., 361, 372 Wilk, B., 569 Wilkerson, J. R., 745 Wilkins, J. A., 358 Wilkinson, C., 562 Wilkinson, D. D., 474 Wilkinson, J. G., 608 Wilkinson, S., 418 Willadsen, A., 296 Williams, A. M., 162, 164, 165, 167, 168, 169, 170, 171, 173, 174, 175, 177, 189, 192, 197, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 224, 227, 228, 229, 230, 255, 265, 266, 275, 277, 279, 290, 293, 300, 392 Williams, C., 31, 43, 45 Williams, D. M., 514, 553 Williams, G., 547, 637 Williams, J. E., 517 Williams, J. G., 167, 203, 204, 205, 209, 210, 211, 212, 213, 214, 216, 218, 225, 227, 229 Williams, J. M., 86, 116, 117, 287, 293, 298, 320, 322, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 395, 396, 397, 398, 399, 416, 417, 418 Williams, K., 692 Williams, L., 6 Williams, M., 126, 142, 145, 148, 164, 194, 225, 227, 229, 395, 408, 726, 728 Williams, P., 144, 496, 498, 499 Williams, T., 847, 848 Williamson, D., 360, 365, 366 Williamson, G. M., 582 Williamson, S., 528, 829
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