10.90.80.70.6V [ Select ]0.50.4Uniform distribution0.30.2Normal distribution0.1Exponential distribution145678.9.10ObservationCumulative Probability For the second graph assume that the x-axisrepresents possible outcomes, and the y-axis represents the probability of theoutcomes.0.4V [ Select ]0.3Uniform distribution0.2Normal distributionExponential distribution0.10.0-3-2-113.2.

In a uniform distribution, the curve is plotted such that the cumulative probability does not change…

Answered: 0.9 0.8 0.7 0.6 [ Select ] 0.5 V 0.4…

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter2: Introduction To Spreadsheet Modeling

Section: Chapter Questions

Problem 20P: Julie James is opening a lemonade stand. She believes the fixed cost per week of running the stand...

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Alternative A1 A2 Prior Probability State of Nature S1 S2 -40 100 0.4 75 0 0.6 There is an option of paying $100 to have research done to better predict which state of nature will occur. When the true state of nature is S1, the research will accurately predict S1 60% of the time. When the true state of nature is S2, the research will accurately predict S2 80% of the time. What is the posterior probability of S2 given that the research predicts S2?
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3. Quarterly sales of a car dealer exhibit an almost constant mean over time, but sales fluctuate depending on the quarter of the year (exhibits seasonality). Quarter Q1 - Spring Q2 - Summer Q3 - Fall Q4 - Winter Mean Year 1 32 74 53 22 45.25 Year 2 30 71 63 15 44.75 Year 3 35 83 45 19 45.5 (a) Using three years' worth of quarterly data that is provided, develop a set of seasonal factors that could be used to forecast car sales (b) Forecast car sales in each of the four quarters (Q1-Q4) of the following year (Year 4), using these seasonal factors.
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The following table shows the three-period moving average and five-period moving average for monthly sales of Budget Furniture's during 2019. Moving averages of Budget Furniture's Time period Months Sales Three-period moving average (rounded off to Five-period moving average four decimals) R'millions 1 Jan 7 5.0000 6.2 February 5.6667 6.6 March 5 7.0000 B 4 April 8.3333 8.2 May 7 9.3333 8.4 June 8.3333 9.6 7 July 12 8.6667 9.6 August 4 A 9.2 September 10 10.6667 10 October 13 10.6667 11 November 9 12 December 10 The seasonal index for the month of February in 2019 is: LO
Quarterly sales of a local department store over a 4-year period are given below: Quarters 2016 Q1 Sales (in millions) 500 Quarters 2018 Q1 Sales (in millions) 560 2016 Q2 490 580 2018 Q2 2018 Q3 2018 Q4 2019 Q1 2019 Q2 2016 Q3 570 630 2016 Q4 2017 Q1 2017 Q2 750 720 480 540 500 590 2017 Q3 580 2019 Q3 620 2017 Q4 740 2019 Q4 790 The data shows sign of seasonality. By computing seasonal indices for each quarter, forecast the sales for each quarter in year 2020, given that the estimated sales in 2020 is

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0.9 0.8 0.7 0.6 [ Select ] 0.5 V 0.4 Uniform distribution 0.3 0.2 Normal distribution 0.1 Exponential distribution 1 2 3 4 6. 7 8. 9. 10 Observation Cumulative Probability