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Select the Brush tool. Select Window > Properties and select a fill color in the Property inspector. See Using the Stroke Color and Fill Color controls in the Property inspector on page 79. Click the Brush Mode modifier and choose a painting mode: Paint Normal paints over lines and fills on the same layer. Paint Fills paints fills and empty areas, leaving lines unaffected. Paint Behind paints in blank areas of the Stage on the same layer, leaving lines and fills unaffected. Paint Selection applies a new fill to the selection when you select a fill in the Fill modifier or the Fill box of the Property inspector. (This option is the same as simply selecting a filled area and applying a new fill.) much like a smart coloring book that never allows you to paint outside the lines. If you start painting in an empty area, the fill doesn t affect any existing filled areas.

Recall from simple linear regression that the regression model was considered significant if MSR was large compared to MSE. The mean-squared regression (MSR) is a measure of the improvement in estimating the response when we include, rather than ignoring, the predictor. If the predictor variable is helpful for estimating the value of the response variable, MSR will be large, the test statistic F = MSR/MSE will also be large, and the linear regression model will be considered signi cant. Signi cance of the coef cients in logistic regression is determined analogously. Essentially, we examine whether the model that includes a particular predictor provides a substantially better t to the response variable than that of a model that does not include this predictor. De ne the saturated model to be the model that contains as many parameters as data points, such as a simple linear regression model with only two data points. Clearly, the saturated model predicts the response variable perfectly, and there is no prediction error. We may then look upon the observed values for the response variable to be the values predicted from the saturated model. To compare the values predicted by our tted model (with fewer parameters than data points) to the values predicted by the saturated model, we use the deviance [2], as de ned here: deviance D = 2 ln likelihood of the tted model likelihood of the saturated model

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The standard guest test is one in which an outside person (known as the plant) is hired by the hotel to experience hotel services and report the ndings to management. This test will enable the front of ce manager to evaluate the sales performance of the front desk clerk. If an unknown plant presents herself with a reservation and is greeted with Yes, we have a reservation; please sign in, the front of ce manager knows that the front desk clerk is disregarding the sales procedure. The front of ce manager should discuss with the employee why the procedure was not followed. Perhaps the goals of the employee have shifted from a larger paycheck to a more reasonable work schedule. Or maybe he or she forgot there was a choice of incentives for the job. Perhaps too many guests were responding negatively to the promotion, and the clerk gave up trying. This information may indicate that unwanted goods or services are being targeted for promotion. When the management of the hotel prepares the written information on the guest comment cards, questions concerning alternative promotion targets should be listed. Choices offered by hotel staff, such as upgrading reservations or information received about restaurants, gift shops, additional reservations, or other areas of the hotel, may be included. This information provides feedback as to whether the suggestions were made and how they were received. There is always the chance that guests may perceive an offer as pushy.

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