The total number of absents students in the four (4) school terms for a matric class at a high school over a 4-year period are recorded and tabulated as shown below. Year Term Number of absents 1 50 2018 2 38 16 4 5 1 67 2019 2 40 3 12 4 8 1 128 2020 2 95 3 37 4 20 1 149 2021 2 133 3 78 4 55 Calculate the first centered moving average for this time series data. A. 27.25 В. 29.375 С. 31.5 D. 32
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Calculate the first centered moving average for this time series data
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- 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 154.5Consider the following time series data Month 1 2 3 4 5 6 7 8 9 10 11 12 Value 90 89 86 91 90 91 88 86 91 93 90 88 a) Construct a time series plot in Excel. Label the axes and graph. c) Develop a smoothing model forecast with α = 0.35. Compute MSE and forecast for month 13 d) Which model is the better predictor? Why?#4) Commuter ridership in Athens, Greece, during the summer months is believed to be heavily tied to the number of tourists visiting the city. During the past 12 years, the data are given in the following table. Year Number of Tourists (millions) Ridership (hundreds of thousands) 1 6 11 2 11 16 3 8 16 4 10 14 5 19 28 6 18 26 7 16 21 8 20 25 9 24 45 10 18 28 11 11 18 12 19 35 a) Create a time series plot for the ridership. b) Using linear regression to see if using the year is a good predictor for the ridership. What is the regression equation? How accurate is the model? c) Using linear regression to see if using the number of tourists is a good predictor for the ridership. What is the regression equation? How accurate is the model? d) Which linear regression equation is better? What is the expected ridership if 10 million tourists visit the city next year? e) Excel File
- Which of the following time series forecasting methods would not be used to forecast seasonal data?The following data set provides the total number of shipments of core major household appliances in the U.S. from 2000 to 2016 (in millions): Year Shipments (millions) 2000 38.4 2001 38.2 2002 40.8 2003 42.5 2004 46.1 2005 47.0 2006 46.7 2007 44.1 2008 39.8 2009 36.5 2010 38.2 2011 36.0 2012 35.8 2013 39.2 2014 41.5 2015 42.9 2016 44.7 a. Plot the time series. b. Fit a three-year moving average to the data and plot the results. c. Fit a five-year moving average to the data and plot the results. d. Compute a linear trend forecasting equation and plot the trend line. e. Compute a quadratic trend forecasting equation and plot the results.Consider the following time series data. Choose the correct time series plot. (i) (ii) (iii) (iv) Plot (ii) What type of pattern exists in the data?Horizontal Pattern Develop a three-week moving average for this time series. Compute MSE and a forecast for week 7. If required, round your answers to two decimal places. Week Time SeriesValue Forecast 1 18 2 13 3 16 4 11 fill in the blank 3 5 17 fill in the blank 4 6 14 fill in the blank 5 MSE: fill in the blank 6The forecast for week 7: fill in the blank 7 Use α = 0.2 to compute the exponential smoothing values for the time series. Compute MSE and a forecast for week 7. If required, round your answers to two decimal places. Week Time SeriesValue Forecast 1 18 2 13 fill in the blank 8 3 16 fill in the blank 9 4 11 fill in the blank 10 5 17 fill in the blank 11 6 14 fill in the blank 12 MSE: fill in the blank 13The forecast for week 7: fill in the blank 14
- Consider the following time series data Month 1 2 3 4 5 6 7 8 9 10 11 12 Value 90 89 86 91 90 91 88 86 91 93 90 88 a) Construct a time series plot in Excel. Label the axes and graph. b) Develop a five month moving average forecast. Compute MSE and a forecast for month 13. c) Develop a smoothing model forecast with α = 0.35. Compute MSE and forecast for month 13 d) Which model is the better predictor? Why?Refer to the gasoline sales time series data in the given table. Compute four-week and five-week moving averages for the time series. Round your answers to two decimal places. Compute the MSE for the four-week and five-week moving average forecasts. Round your intermediate calculations and final answers to two decimal places. What appears to be the best number of weeks of past data (three, four, or five) to use in the moving average computation? Recall that MSE for the three-week moving average is 12.89.Using 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.
- Using the time series data in the table, respond to the following items. Period Sales 1 $ 615 2 678 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 d-1. Compute all possible forecasts using a trend forecasting model using simple linear regression? (Round your answers to 3 decimal places.) Period Sales Predicted Sales Absolute Error 1 615 2 678 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 d-2. What is the MAD? (Round your answer to 3 decimal places.) d-3. What is the trend equation based on the regression analysis? (Round your answers to 3 decimal places.) Sales = __________ + _______________ time…The quarterly sales data (number of copies sold) for Moraga Bookstore over the past three years follow. Year Quarter 1 Quarter 2 Quarter 3 Quarter 4 1 16 25 22 15 2 19 31 25 18 3 21 33 27 17 Construct a time-series plot. What type of pattern(s) exists in the data? Compute the seasonal index for the four quarters. ]Compute the linear trend equation for the deseasonalized data.Consider the following gasoline sales time series data. Click on the datafile logo to reference the data. Week Sales (1000s of gallons) 1 16 2 20 3 20 4 23 5 18 6 17 8 19 9 23 10 19 11 14 12 21 a. Using a weight of 1/2 for the most recent observation, 1/3 for the second most recent observation, and 1/6 the most recent observation, compute a three-week weighted moving average for the time series (to 2 decimals). Enter negative values as negative numbers. Week Time-Series Value Weighted MovingAverage Forecast ForecastError (Error)2 1 2 3 4 5 6 7 8 9 10 11 12 Total b. Compute the MSE for the weighted moving average in part (a).MSE = Do you prefer this weighted moving average to the unweighted moving average? Remember that…