Analyse the time series of the following supermarket sales data and present the results in graphical form, including a forecast for the daily sales in week 5. Supermarket sales (K000) for a particular period
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Analyse the time series of the following supermarket sales data and present the results in graphical form, including a forecast for the daily sales in week 5.
|
Supermarket sales (K000) for a particular period |
||||
|
|
Week 1 |
Week 2 |
Week 3 |
Week 4 |
|
Monday |
22 |
22 |
24 |
26 |
|
Tuesday |
36 |
34 |
38 |
38 |
|
Wednesday |
40 |
42 |
43 |
45 |
|
Thursday |
48 |
49 |
49 |
50 |
|
Friday |
61 |
58 |
62 |
64 |
|
Saturday |
58 |
59 |
58 |
58 |
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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 154.5The 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.Refer to the gasoline sales time series data in the given table. Week Sales (1000s of gallons) 1 18 2 22 3 18 4 22 5 18 6 16 7 20 8 19 9 23 10 21 11 14 12 23 Compute four-week and five-week moving averages for the time series. If required, round your answers to two decimal places. Week Sales 4 PeriodMoving Average 5 PeriodMoving Average 1 18 2 22 3 18 4 22 5 18 6 16 7 20 8 19 9 23 10 21 11 14 12 23 Compute the MSE for the four-week and five-week moving average forecasts. If required, round your intermediate calculations to four decimal places and round your final answers to three decimal places.MSE for four-week moving average = MSE for five-week moving average =
- Consider the following time series data Month 1 2 3 4 5 6 7 8 9 10 11 12 Value 80 82 84 83 83 84 85 84 82 83 84 83 a) Construct a time series plot in Excel. Label the axes and graph. b) Develop a three month moving average forecast. Compute MSE and a forecast for month 13. c) Develop an exponential smoothing forecast with a = 0.35. Compute MSE and a forecast for month 13.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?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 Hungry Person restaurant has posted its sales (in thousands of dollars) for the last two years. Month 1st Year 2nd Year January 201 216 February 216 223 March 232 240 April 201 244 May 221 232 June 213 220 July 220 211 August 214 213 September 202 217 October 217 220 November 224 212 December 236 216 a) Construct a time series plot in excel. (Label axes and graph) b) Develop a four month moving average. Compute MSE and forecast the amount of sales for the next month. c) Use α = 0.2 to compute the exponential smoothing values. Compute MSE and forecast for the next month. d) Compare the result for the four month average and exponential smoothing. Which appears to provide a better forecast based on MSE? Explain.Refer to the gasoline sales time series data in the given table. Week Sales (1000s of gallons) 1 17 2 22 3 19 4 24 5 19 6 16 7 22 8 19 9 22 10 20 11 16 12 22 Compute four-week and five-week moving averages for the time series. If required, round your answers to two decimal places. Week Sales 4 PeriodMoving Average 5 PeriodMoving Average 1 17 2 22 3 19 4 24 5 19 fill in the blank 1 6 16 fill in the blank 2 fill in the blank 3 7 22 fill in the blank 4 fill in the blank 5 8 19 fill in the blank 6 fill in the blank 7 9 22 fill in the blank 8 fill in the blank 9 10 20 fill in the blank 10 fill in the blank 11 11 16 fill in the blank 12 fill in the blank 13 12 22 fill in the blank 14 fill in the blank 15 Compute the MSE for the four-week and five-week moving average forecasts. If required, round your intermediate calculations to four decimal places and round your final answers to three decimal places.MSE…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. Week Sales (1,000s of gallons) 1 18 2 22 3 15 4 24 5 18 6 15 7 21 8 19 9 21 10 20 11 16 12 22 (a) Compute four-week and five-week moving averages for the time series. If required, round your answers to two decimal places. Week Sales 4 Period Moving Average 5 period Moving Average 1 18 2 22 3 15 4 24 5 18 6 15 7 21 8 19 9 21 10 20 11 16 12 22 (b) Compute the MSE for the four-week and five-week moving average forecasts. If required, round your final answers to three decimal places. MSE for four-week moving average = MSE for five-week moving average = (c) What appears to be the best number of weeks of past data (three, four, or five) to use in the moving average computation? Consider that the MSE for the three-week moving average is 10.086.The elevation of a lake surface (feet above sea level) varies according to the annual flow of a river that feeds it. A geological survey provided the following data from equally spaced intervals of time over a 15 year period. Time Period Elevation 1 4817 2 4819 3 4824 4 4822 5 4826 6 4831 7 4836 8 4837 9 4839 10 4837 11 4832 12 4827 13 4823 14 4818 15 4817 Make a time-series graph displaying the data.Refer to the gasoline sales time series data in the given table. Week Sales (1,000s of gallons) 1 18 2 22 3 15 4 26 5 18 6 17 7 21 8 19 9 22 10 18 11 17 12 21 (a) Compute four-week and five-week moving averages for the time series. If required, round your answers to two decimal places. Week Sales 4 PeriodMoving Average 5 periodMoving Average 1 18 2 22 3 15 4 26 5 18 6 17 7 21 8 19 9 22 10 18 11 17 12 21 (b) Compute the MSE for the four-week and five-week moving average forecasts. If required, round your final answers to three decimal places. MSE for four-week moving average = MSE for five-week moving average = (c) What appears to be the best number of weeks of past data (three, four, or five) to use in the moving average computation? Consider that the MSE for the three-week moving average is 11.407.