\"Assignment #3\" Solve the following questions on Forecasting. (Show your calculations for all answers) 1. The following is the number of customers who checked in at Hotel A in the past 6 weeks: Week of Customers checked-in August 31 360 September 7 446 September 14 386 September 21 415 September 28 275 October 5 584 a) Forecast the demand for the week of October 12 using a 3-week moving average. b) Use a 3-week weighted moving average, with weights of .2,.4, and .8, using .8 for the most recent week. Forecast demand for the week of October 12. c) Compute the forecast for the week of October 12 using exponential smoothing with a forecast for August 31 of 360 and =.2. *Hint: You should first calculate the new forecast with the given forecast for August 31 of 360 as following using the exponential smoothing formula: 360 + .2(360-360) =360. Then you forecast the next week, September 7, based on the new forecast 360 and so on and so on. So you keep on forecasting from thereon until October 12. 2. Forecast May,2015 group sales for Hotel A using each of the following: Month Group Sales May, 2014 36 June, 2014 53 July, 2014 60 August, 2014 65 September, 2014 45 October, 2014 38 November, 2014 40 December, 2014 52 January, 2015 68 February, 2015 29 March, 2015 51 April, 2015 43 a) Nave method b) A 3-month moving average c) A 6-month weighted average using .1,.2,.3,.2,.1, and .4, with the weights applied respectively. d) Exponential smoothing using an =.4 and a January, 2015 forecast of 55. 3. Consider the following actual and forecast demand levels for Big Mac hamburgers at a local McDonald's restaurant: Day Actual demand Forecast demand Monday 88 88 Tuesday 72 88 Wednesady 68 84 Thursday 48 80 Friday The forecast for Monday was derived by observing Monday's demand level and setting Monday's forecast level equal to this demand level. Subsequent forecasts were derived by using exponential smoothing with a smoothing constant of 0.25. Using this exponential smoothing method, what is the forecast for Big Mac demand for Friday? Causal (Associative) Forecasting; Regression Analysis 4. Hotel A wants to see the impact of three independent variables, loyalty program tier (X1), price (X2), and location (X3), on its revpar (Y). The multiple regression line caluculated is as following: Y = 2.5 + 2.1(X1) + 0.8(X2) - 4.5(X3) a b Forecast Hotel A's revpar for the following conditions a and b: X1 (loyalty program tier) X2 (price) X3 (location) 2 299 3.5 4 199 6.5 5. The number of employee accidents from the housekeeping department in Hotel A is related to bed height, bed size, and spinal loading. The linear regression model formula has been calculated as following: Y = 3.9+ 8.5(X1) + 2.9(X2) + 5(X3). a b c Calculate the expected number of employee accidents under conditions a, b, and c: X1 (bed height) X2 (bed size) X3 (spinal loading) 4 5 6 8 3 4 9 2 0 hey use 7 percent of their available revolving credit, their oldest credit account was opened an average of 25 years ago, and they have an average of seven credit cards, including both opened and closed accounts