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with an analogous form for nonchurners [Here exp(y) represents e y Also, f T |C (t) is substituted for p(T = t|C) since for continuous random variables, p(T = t) = 0, t] Next, suppose we are interested in classifying new customers who have 800 total minutes and belong to both plans, using naive Bayes classi cation We have for churners, p(I V T = 800|C)P(C) = P(I |C)P(V |C) f T |C (800)P(C) = 02836(01656)(0001191)(01449) = 0000008105 and for nonchurners, p(I V T = 800|C)P(C) = P(I |C)P(V |C) f T |C (800)P(C) = 00653(02954)(00001795)(08551) = 0000002961 Hence, the naive Bayes classi cation for new customers who have 800 total minutes and belong to both plans is churn = true by a posterior odds ratio of 0000008105 = 274 0.

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Tax Strategy Importantly, this survey shows a strong correlation between a company s tax strategy and its effective tax rate. This shows that controllers can have significant cost-saving effects on their employers tax charges. In this study, PwC asked participants to evaluate how actively they managed their tax profile, using a scale of 1 (not managed) to 10 (high levels of active planning). The breakdown: 3 rating, 5% of sample; 4 rating, 5% of sample; 5 rating, 15%; 7 rating, 20%; 8 rating, 35%; 9 rating, 10%; and 10 rating, 15%. Most companies believe they are managing their tax obligations fairly actively, with an average rating of 7.4. Further, 80% of respondents rated themselves 7 or higher in the self-appraisal section. PwC then compared a company s self-evaluation of its tax planning with its effective tax rate. It observes: There is a strong correlation between higher active tax management and a lower effective tax rate. This simply demonstrates what all tax directors know there are plenty of opportunities in most companies to manage the tax charge. There also appears to be a correlation between lower effective tax rates and companies where nontax members of finance are evaluated on the tax results. It seems that if the CFO and his team are appraised on posttax results, they are more incentivized to ensure that the tax charge is effectively managed.

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The J4L Barcodes are integrated in Eclipse Birt 4.3 or later. The components support 1D barcodes, PDF417, Datamatrix , QRCode, Azteccode and Maxicode.

Using the Markov inequality (10.4) and (10.14), we can write Pr[X 1] Pr[X 4f E[X]] 1 4f (10.15)

000002961 In other words, the additional information that the new customer had 800 total minutes was enough to reverse the classi cation from churn = false (previously, without total minutes) to churn = true This is due to the somewhat heavier cell-phone usage of the churners group, with a higher mean total minutes Assumptions of normality should not be made without supporting evidence Should we take Figure 54 as supporting evidence for the normality of our distributions No The histogram in Figure 54 represents the collection of all churners and nonchurners lumped together, not the individual distributions for each category that we would like to work with Therefore, Figure 54 does not represent supporting evidence for our assumptions Consider Figure 55, a Minitab comparison dot plot of the two distributions Immediately we can see that indeed there are many more nonchurner records than churners.

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We also note that the balancing point (the mean, indicated by the triangle) for churners is greater than for nonchurners, supporting the statistics above Finally, we notice that the normality assumption for nonchurners looks quite solid, whereas the normality assumption for the churners looks a little shaky Normal probability plots are then constructed for the two distributions just described and shown in Figure 56 In a normal probability plot, there should be no systematic deviations from linearity; otherwise, the normality assumption is called into question In Figure 56 the gray points represent total minutes for churners, and the black points represent total minutes for nonchurners Note that the bulk of the.