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Statistical KBIs use statistical (regression) formulas to forecast future business volumes by analyzing historical information. Most statistical KBIs are created automatically by Watson the system when you create revenue center periods and market segments. All revenue center periods, market segment groups, rooms, and arrivals are statistical KBIs. However, you must complete their regression formulas. The only way to manually create a Statistical KBI is to select Separate as the Type in the General tab.

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Regression also allows you to use multiple independent factors.  In other words, when forecasting meal covers, you could ask the software to analyze hotel guests, banquet guests, and any other independent factor that may show a relation. Thus, the statistical model determines how effective an independent factor is in predicting a dependent factor and, based on the relationship, predicts it again. Of course, if the prediction of the independent factor is inaccurate, the subsequent prediction of the dependent factor will be inaccurate. In this case Watson , LMS will return a predicted volume forecast zero (o). Keep in mind that the system needs approximately 20 weeks of past history to begin to accurately determine the relationship between the dependant and independent factors. Occasionally, Watson may the system will return values for many of the forecast days and zeros for the others. That simply means that there is a relationship on some days of the week and not on others.  This does not mean the use of the statistical model is wrong. It might suggest the need for additional past history to return a value.

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KBIX = A(KBIA + KBIB -- KBIC) + B(KBID) + C

Watson LMS lets you create KBIs for factors whose impact you want to assess without assuming a perfect one-to-one relationship between one guest factor and the available guest market. For example, local guests attending AM meetings may use a restaurant for breakfast, but it is difficult to determine what percentage will do so. For this reason, the local guest market is more difficult to measure than in-house guests. In this case, you can create a statistical KBI to assess the impact of local guests as an independent variable.

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