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Regression statistics may be presented succinctly in an ANOVA table, the general form of which is shown in Table 2.5. Here m represents the number of predictor variables, so that for simple linear regression, m = 1. The ANOVA table conveniently displays the relationships among several statistics, showing for example that the sums of squares add up to SST. The mean squares are presented as the ratios of the items to their left, and for inference, the test statistic F is represented as the ratio of the mean squares. Tables 2.6 and 2.7 show the Minitab regression results, including the ANOVA tables, for the orienteering and cereal examples, respectively.

The average room rate, like the average check, is only an average and not necessarily the rate for any classification of rooms. Normally, the average room rate is broken down into an average rate for single rooms and average rate for double rooms. Room rates are also calculated based on the square footage of rooms with different sizes. Total room revenue is a combination of average room rate and actual room occupancy. Therefore, one should keep in mind the occupancy of rooms by day of the week, because declining room rate can be compensated for by increasing room occupancy, and vice versa. In room rate discounting, an equation can be used to calculate the equivalent occupancy needed to hold total sales revenue less marginal costs constant if the rack rate is discounted. The equation to calculate the equivalent occupancy is: Equivalent occupancy Original occupancy Rack rate Marginal cost [Rack rate (1 Discount %)] Marginal cost

The fastest way to lose revenue is to not take consumer privacy seriously No amount of marketing, attractive pricing, or convenience will entice a consumer to conduct business online or of ine if they believe that in conducting that business, their personal information will be compromised If you do any business online today, you will see that the use of a privacy policy is now the norm This has not always been the case As consumer awareness heightened with big stories on thefts of card data, business awareness also increased to make sure they were doing things to ease that consumer concern Over the last 10 years, there have been a lot of third-party organizations that have entered the scene to attempt to enforce privacy policies, such as TRUSTe, BBBOnline, and others.

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It is interesting to note that whereas the expectation is for the globally optimal parameters to ensure a lower error as compared with the locally optimal parameters, which have the advantage of higher interpretability (Yen et al., 1998), the test results indicate that this is not always the case. Several benchmark problems were tested (courtesy of Mr. X. Zhou, who performed the tests) using both local and global optimality criteria. Figure 2.8 presents the results of the test in which both models start learning from scratch, learning during the whole data stream (t k, there is no separate validation data set, and after each sample a prediction is made and at the next time step the true value of the output is used for updating the eTS ).

Businesses today are concerned not only with soothing consumer concerns to keep them coming back, but also about their ability to use consumer information in fraud prevention and investigations of crime They are also concerned about the repercussions of using that data In using it, will it leave them open to future litigation Will it instigate hacking attacks or spur some type of governmental investigations None of these activities is good for business Consumers and businesses must work together to make protection of consumer data easier and safer for conducting business As a business today, provide your consumer with choices, and as a consumer, take the time to understand what you are agreeing to when you are exploring the web We all need to hope that government will take a slow and deliberate path on any sweeping legislation to allow the new commerce channels, such as eConsumer, to grow up.