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- Consider the following time series data. a. Show the four-quarter and centered moving average values for this time series (to 3 decimals if necessary). b. Compute seasonal indexes and adjusted seasonal indexes for the four quarters (to 3 decimals).Which of the following time series forecasting methods would not be used to forecast seasonal data?consider the following time series data.Month 1 2 3 4 5 6 7Value 24 13 20 12 19 23 15a. compute MSe using the most recent value as the forecast for the next period. Whatis the forecast for month 8?b. compute MSe using the average of all the data available as the forecast for the nextperiod. What is the forecast for month 8?c. Which method appears to provide the better forecast?
- Consider the following time series data: 1 2 3 4 5 6 7 26 15 22 14 21 25 17 PART 1.Compute MSE using the most recent value as the forecast for the next period and then calculate the forecast for month 8. PART 2.Compute MSE using the average of all the data available as the forecast for the next period. What is the forecast for month 8?Consider the following time series data. Week 1 2 3 4 5 6 Value 18 13 16 11 17 14 Construct a time series plot. What type of pattern exist in the data? Develop a three-week moving average for this time series. Compute MSE and forecast for week 7. Use a = 0.2 to compute the exponential smoothing values for the time series. Compute MSE and forecast for week 7.Which of the time series forecasting methods would not be used to forecast seasonal data?
- 1. Consider the following time series: a. Construct a time series plot. What type of pattern exists in the data? b. Use simple linear regression analysis to find the parameters for the line that minimizes MSE for this time series.Develop seasonal factors from the table below by following a 3-MA values for the time series data t 1 2 3 4 5 6 7 8 9 y 13 18 13 14 13 16 14 21 13Using the time series data in the table, respond to the following items. Period Sales 1 $ 615 2 676 3 761 4 710 5 784 6 801 7 852 8 698 9 1,193 10 1,115 11 1,231 12 1,259 13 1,495 14 1,229 15 1,652 16 1,337 17 1,673 18 1,613 Please show work, thanks.
- Consider the following (actual real-world) data of total cumulative coronavirus cases diagnosed in the United States by the following days: Sunday, 3/15 -- 3613 Monday, 3/16 -- 4596 Tuesday, 3/17 -- 6344 Wednesday, 3/18 -- 9197 Thursday, 3/19 -- 13779 Friday, 3/20 -- 19367 Saturday, 3/21 - 24192 Sunday, 3/22 - 33592(a) Does this data suggest exponential growth in the total number of cases diagnosed as function of the number of days that have elapsed? Explain how you would use the data to support your conclusion. (b) Let P(t) denote the total number of people in the US who have tested positive for the coronavirus on or before t days after March 15. Let P0=3613, the total number of people in the US who had tested positive for coronavirus by March 15. Then if we use an exponential growth model, P(t)=P0ekt, how would you use the above data to estimate the value of k? (c) Based on this data alone , how many total coronavirus cases would you expect to be diagnosed in the United States by…The following ratio-to-moving averages for the seasonally adjusted series were found by the decomposition method applied on a time series representing quarterly sales for January 2018 to December 2020 period: a. Calculate the Seasonal Index for every quarter. b. If the trend is described by the trend line T^ = 1,000 + 30 t, what is the forecast for the fourth quarter of 2021?The table below contains the average price paid for a new home in a certain area from 2000 to 2010. a. Construct a time-series plot of new home prices. b. What pattern, if any, is present in the data? Year Average_Price_($_thousands)2000 351.12001 330.52002 310.52003 296.72004 229.72005 182.32006 154.52007 156.32008 154.72009 154.52010 154.5