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predicted house count: an estimate of the number of guests expected to register based on previous occupancy activities printer: computer hardware in dot-matrix, ink-jet, or laser models that produces hard copies of output data in letter quality or draft style in various print fonts, with printing speed being expressed in CPS (characters per second), number of characters per line, and pages per minute and paper insertion being tractor-fed, single-sheet, or continuous-form prior approved credit: use of a credit card to establish creditworthiness private label cards: credit cards issued by a retail organization, such as a department store or gasoline company processor speed: how fast a CPU (central processing unit) makes calculations per second; expressed in MHz (the abbreviation for megahertz ) pro t-and-loss statement: a listing of revenues and expenses for a certain time period property management system (PMS): a generic term used to describe applications of computer hardware and software used to manage a hotel by networking reservation and registration databases, point-of-sale systems, accounting systems, and other of ce software psychographic data: emotional and motivational forces that affect a service or product for potential markets rack rate: the highest room rate category offered by a hotel real estate investment trust (REIT): a form of nancing an investment in real estate through a mutual fund recreation director: the person who is in charge of developing and organization recreational activities for guests referral member: a hotel owner or developer who has access to the national reservation system referral property: a hotel operating as an independent that wishes to be associated with a certain chain; uses national reservation system referral reservation service: a service offered by a management company of a chain of hotels to franchisee members registration card: a form on which the guest indicates name, home or billing address, home or billing phone number, vehicle information, date of departure, and method of payment reservation code: a sequential series of alphanumeric combinations that provide the guest with a reference for a guaranteed reservation reservation referral system: a worldwide organization that processes requests for room reservations at a particular member-hotel reservations manager: the person who takes and con rms incoming requests for rooms, noting special requests for service; provides guest with requested information; maintains an accurate room inventory; and communicates with marketing and sales.

1.00, with both endpoints of the con dence interval also reported as 1.00. Do we conclude from this that capnet is not signi cant If so, how do we resolve the apparent contradiction with the strongly signi cant z-test p-value of approximately zero Actually, of course, there is no contradiction. The problem lies in the fact that the odds ratio results are reported only to two decimal places. More detailed 95% con dence intervals are provided here: CI(ORcapnet ) = exp[b1 z SE(b1 )] = exp [0.0002561 (1.96)(0.0000079)] = (e0.0002406 , e0.0002716 ) = (1.000241, 1.000272) Thus, the 95% con dence interval for the capnet variable does not include the null value of e0 = 1, indicating that this variable is in fact signi cant. Why is such precision needed Because capnet is measured in dollars. One additional dollar in capital gains, for example, would presumably not increase the probability of a high income very dramatically hence, the tiny but signi cant odds ratio. However, nearly 87% of the records have zero capnet (neither capital gains nor capital losses). What effect would this have on the linearity assumption Table 4.14 provides the income-level counts for a possible categorization of the capnet variable. Note that high income is associated with either capnet loss or capnet gain $3000, while low income is associated with capnet none or capnet gain < $3000. Such relationships are incompatible with the assumption of linearity. We would therefore like to rerun the logistic regression analysis, this time using the capnet categorization shown in Table 4.14.

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vations to travelers and lodging establishments Overview of reservation system Sources of reservations Forecasting reservations

3 for Var_A, which is relatively normally distributed, and Var_C, which is not The normal curve overlay is selected along with the frequency histogram to assist the judgment of normality The positive skewness of Var_C is readily apparent Normal probability plots (sometimes called normal quantile-quantile, or QQ, plots) require some explanation In these plots, the scores are rst sorted and ranked Then an expected normal value is computed and plotted against the actual normal value for each case The expected normal value is the z score that a case with that rank holds in a normal distribution; the actual normal value is the z score it has in the actual distribution If the actual distribution is normal, the two z scores are similar, and the points fall along the diagonal, running from lower left to upper right Deviations from normality shift the points away from the diagonal.

[28] Warwick K.: Wiring in Humans: Advantages and problems as humans become part of the machine network via implants . Presentation in SWAMI Final Conference in Brussels, 21 22 March, 2006. Summary of the presentation in Friedewald M. and Wright D.: Safeguards in a World of Ambient Intelligence (SWAMI) , Deliverable D5, Report on the Final Conference, Brussels, 21 22 March 2006. [29] Wilhemsson T.: Kertomus parisuhteesta ja sopimuksesta . Oikeus, Vol. 1, 1999, pp. 66 74. [30] Wilhelmsson T. and J askinen N.: Avoimet yhti t ja kommandiittiyhti t. Kauppakaari Lakimiesliiton kustannus, a o o 2001.

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When normal probability plots are inspected side by side, the equivalence of the standard deviations is also assessed by looking at the slope of the pattern of points for each distribution; when the slopes for several distributions are relatively equal, so are the standard deviations (Cleveland, 1993) This can be useful for evaluating homogeneity of variance in grouped data..

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