1. The company has developed an adjusted exponential smoothing…

Question1. The company has developed an adjusted exponential smoothing…1. The company has developed an adjusted exponential smoothing with α=0.20, β=0.20 to forecast the occupancy rate. The actual rates and the forecasts from the model are as follows: Year Occupancy Rate (%) AES α=0.20, β=0.201 83 83.02 78 83.03 75 81.84 81 80.25 86 80.36 85 81.77 89 82.58 90 84.09 86 85.510 —– 85.6  a) Develop a linear tend line forecast.  Year-x Occupancy Rate-y (%) xy x21 83 Answer Answer2 78 Answer Answer3 75 Answer Answer4 81 Answer Answer5 86 Answer Answer6 85 Answer Answer7 89 Answer Answer8 90 Answer Answer9 86 Answer Answer∑x=Answer ∑y=Answer ∑xy=Answer ∑x2=Answer       n=Answer              1. xbar=Answer2. ybar=Answer 2 d.p.3. b=Answer to 2 d.p.4. a= Answer to the nearest whole number 5. Linear Trend Equation y=Answer+Answerx6. Forecast the occupancy rate for the year 10 to the nearest whole percentage.Answer%  b) Compare both forecasts, using MAD, cumulative error, and average error. Round all errors to 2 d.p. AES α=0.20, β=0.20 Linear TrendCumulative Error= Cumulative Error=Answer AnswerAverage Error= Average Error=Answer AnswerMAD= MAD=Answer AnswerMAPD= MAPD=Answer% Answer% c) Indicate which one seems to be most accurate.  AESLinear Trend  d) Select the true statement:  Both forecasts exhibit a high biasBoth forecasts exhibit a low biasNone of the forecasts exhibit any bias2.  A. Fill in the table.     Orders(1,000s)/Year        Quarters 1 2 3 4 5 TotalsJanuary-March 18.6 18.1 22.4 23.2 24.5 AnswerApril-June 23.5 24.7 28.8 27.6 31 AnswerJuly-September 20.4 19.5 21 24.4 23.7 AnswerOctober-December 41.9 46.3 45.5 47.1 52.8 AnswerTotals Answer Answer Answer Answer Answer Answer  B. Develop a seasonally adjusted forecast model for theses order data. Part 1. Calculate All seasonal factors rounded to 2 d.p. SF1=AnswerSF2=AnswerSF3=AnswerSF4=AnswerTotal of all seasonal factors: AnswerPart 2. Develop a linear trend line model and forecast estimate for orders in year 6. Year Orders(1,000s)/Year xy X21 Answer Answer Answer2 Answer Answer Answer3 Answer Answer Answer4 Answer Answer Answer5 Answer Answer Answern=Answer      ∑x=Answer ∑y=Answer ∑xy=Answer1d.p. ∑x2=Answerxbar=Answer ybar=Answer    b=Answer2 d.p.      a=Answer2 d.p.       a. Linear trend equation: y=Answer+Answerx b. Forecast estimated for orders in year 6.FY6 in 1,000s orders=Answer 2 d.p.Part 3. Forecast demand for each quarter for year 6 (using a linear trend line forecast estimate for orders in year 6.)Round each to 3 d.p. SAF1=Answer 1,000s ordersSAF2=Answer 1,000s ordersSAF3=Answer 1,000s ordersSAF4=Answer 1,000s ordersTotal number of orders ( round to 2 d.p.) = Answer 1,000s orders  3.A. Develop a linear regression model for theses data and forecast the ice cream consumption if average weekly daytime temperature is expected to be 85 degrees.  Fill the table:  Week Average Temperature (degrees) Ice Cream Sold(gal.) xy x2 y21 73 110 Answer Answer Answer2 65 95 Answer Answer Answer3 81 135 Answer Answer Answer4 90 160 Answer Answer Answer5 75 97 Answer Answer Answer6 77 105 Answer Answer Answer7 82 120 Answer Answer Answer8 93 175 Answer Answer Answer9 86 140 Answer Answer Answer10 79 121 Answer Answer Answern=Answer ∑ x=Answer ∑y=Answer ∑xy=Answer ∑ x2=Answer ∑y2=Answer             Xbar=Answer1 d.p Ybar=Answer1d.p.                 b=Answerto 2 d.p .        a=Answer to 2 d.p.             Linear Regression Model: y=Answer+AnswerxIce cream consumption if average weekly daytime temperature is expected to be 85 degrees is:  Answer round to nearest whole number of gallons.  B. Determine the strength of the linear relationship between temperature and ice cream consumption by using correlation.  r=Answer round to 2 d.p.Is the correlation: Strong positive correlationVery strong negative correlationStrong negative correlationVery strong positive correlation C. Find and interpret the determination coefficient r2r2    =Answer% round to the whole number.Very strong positive determination coefficientStrong determination coefficientPercent variation in average weekly temperature that result from ice cream soldPercent variation in ice cream sold that result from average weekly temperature MathStatistics and ProbabilityMNG 1201Share Question