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5. Give an example of: a. A hotel departmental budget b. A capital budget for a restaurant 6. Explain the difference between a fixed and a flexible budget. 7. Two of the steps in the budgeting cycle are: a. Establishing attainable goals b. Planning to achieve these goals What are the other three steps 8. Discuss three possible limiting factors to consider in preparing a budget for a hotel or restaurant. 9. A cocktail lounge had sales revenue in May of $40,000. Budgeted revenue was $42,000. List three possible questions that could be asked, the answers to which might explain the $2,000 difference. 10. In projecting sales revenue for breakfast in the coffee shop of a hotel, what factors need to be considered 11. What is derived demand 12. List the four items that must be multiplied by each other to forecast total annual food revenue for the dinner period of a restaurant. 13. What is a pro forma income statement 14. List three types of cost that are controllable with ZBB. 15. Give an example of a decision unit in a hotel s accounting office and write a one-sentence objective for that decision unit. 16. Briefly describe the ranking process under ZBB. 17. Give two advantages and two disadvantages of ZBB. 18. Briefly explain how the use of a moving average works as a forecasting method. 19. Using an example, explain what an unfavorable cost variance is. 20. Using an example, explain what a favorable sales volume variance is. 21. Explain the difference between a sales volume variance and a quantity variance.

WHAT IS MULTIPLE REGRESSION ANALYSIS Multiple regression analysis (MR) is a general system for examining the relationship of a collection of independent variables to a single dependent variable. It is among the most extensively used statistical analyses in the behavioral sciences. Multiple regression is highly exible and lends itself to the investigation of a wide variety of questions. The independent variables may be quantitative measures such as personality traits, abilities, or family income; or they may be categorical measures such as gender, ethnic group, or treatment condition in an experiment. In the most common form of multiple regression analysis, which we consider here, the dependent variable is continuous. The basic ideas of multiple regression can be extended to consider other types of dependent variables such as categories, counts, or even multiple dependent variables. The relationship between an independent

Figure 5.16: The DER performance of the SD-MSE algorithm characterized by Equation 5.6. The system parameters of Table 5.1 were utilized and the AQAM switching thresholds were set according to Table 4.8 for the target BER of 1%. The DER measure was defined in Section 5.6.1 and a conventional binary BCH(31,26, coding scheme was utilized. 3)

Context variables are also commonly referred to as context elements, context features, context pieces or context atoms A context variable is a column in the table of the generated data table Examples of context variables: body temperature, GPS location, time, identi cation of the person, identi cation of the phone User contexts can be understood from a statistical point of view as clusters in the data In this tool, user contexts are created by selecting several context variables, and giving them statistical properties User contexts are often referred to simply as context or situation In other words, user contexts are situations where one expects the context variables to have similar statistical properties, such as similar probability distributions Examples of user contexts: person is walking, person is running, car is running, people grouped in a room, device is used, device is not used.

Scenarios de ne how user contexts are grouped together Scenarios contain all the data that the context simulation tool needs to generate the data Examples of a scenario: a person is going home (the scenario consists in successive user contexts: person is walking outside, person is sitting in the bus, person is walking outside) Figure 47 shows a screenshot of the graphical interface of the tool In the left part, the three different categories of the con guration le are shown In the right part, the information about the context variable activity is displayed One can see that activity is represented as having three different values: [0=walking, 1=running, 2=stationary] In Figure 48, the con guration of the user context Walking outside in Ruoholahti is displayed For each of the context variables that one associates to this user context, the statistical distribution that corresponds to it is de ned.

The probability can either come from prior analysis of real sensor datasets, or one can guesstimate them In the given example, the context variable activity is given for Walking outside in Ruoholahti with the statistical distribution [walking=80%, running=15%, stationary=5%] The advantage of this method is that it is easy to include noise, error and accounts for variations in the environment..