Consider the following time series data. Week 1 2 3 4 5 6 Value 17 14 17 11 16 15 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 Value Forecast 1 17 2 14 3 17 4 11 5 16 6 15 MSE: The forecast for week 7: Use trial and error to find a value of the exponential smoothing coefficient α that results in a smaller MSE than what you calculated for α = 0.20. Use a two-decimal digit precision. alpha:

Consider the following time series data. Week 1 2 3 4 5 6 Value 17 14 17 11 16 15 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 Value Forecast 1 17 2 14 3 17 4 11 5 16 6 15 MSE: The forecast for week 7: Use trial and error to find a value of the exponential smoothing coefficient α that results in a smaller MSE than what you calculated for α = 0.20. Use a two-decimal digit precision. alpha:

Algebra & Trigonometry with Analytic Geometry

13th Edition

ISBN:9781133382119

Author:Swokowski

Publisher:Swokowski

Chapter5: Inverse, Exponential, And Logarithmic Functions

Section5.6: Exponential And Logarithmic Equations

Problem 70E

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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. 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.
Consider the following time series data. Quarter Year 1 Year 2 Year 3 1 4 6 7 2 0 1 4 3 3 5 6 4 5 7 8 (a) Choose the correct time series plot. (i) (ii) (iii) (iv) What type of pattern exists in the data? (b) Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data: Qtr1 = 1 if Quarter 1, 0 otherwise; Qtr2 = 1 if Quarter 2, 0 otherwise; Qtr3 = 1 if Quarter 3, 0 otherwise. If required, round your answers to three decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank (Example: -300). If the constant is "1" it must be entered in the box. Do not round intermediate calculation. ŷ = + Qtr1 + Qtr2 + Qtr3 (c) Compute the quarterly forecasts for next year based on the model you developed in part (b). If required, round your answers to three decimal places. Do not round…
Consider the following time series data. Month 1 2 3 4 5 6 7 Value 24 13 20 12 19 23 15 Using the Exponential Smoothing method to forecast the next period, compute the MSE (Mean square Error). Consider: the smoothing constant (α) = 0.5 Which of the following options is the answer? Select one:a. 41.24b. 39.95c. 40.05d. 41.02
Consider the following time series data Week 1 2 3 4 5 6 Value 18 13 16 11 17 14 a. Construct a time series plot. What type of pattern exists in the data?b. Develop the three-week moving average forecasts for this time series. compute MSE and a forecast for week 7.c. Use α = .2 to compute the exponential smoothing forecasts for the time series.Compute MSE and a forecast for week 7.d. Compare the three-week moving average approach with the exponentialsmoothing approach using α = .2. Which appears to provide more accurate forecasts based on MSE? explain.e. Use a smoothing constant of α = .4 to compute the exponential smoothing forecasts. does a smoothing constant of .2 or .4 appear to provide more accurate forecasts based on MSE? explain.
Consider the following time series data. Quarter Year 1 Year 2 Year 3 1 4 6 7 2 0 1 4 3 3 5 6 4 5 7 8 (b) Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data: Qtr1 = 1 if Quarter 1, 0 otherwise; Qtr2 = 1 if Quarter 2, 0 otherwise; Qtr3 = 1 if Quarter 3, 0 otherwise. If required, round your answers to three decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank (Example: -300). If the constant is "1" it must be entered in the box. Do not round intermediate calculation. ŷ = + Qtr1 + Qtr2 + Qtr3 (c) Compute the quarterly forecasts for next year based on the model you developed in part (b). If required, round your answers to three decimal places. Do not round intermediate calculation. Year Quarter Ft 4 1 4 2 4 3 4 4 (d) Use a multiple…
Consider the following time series data: Month 1 2 3 4 5 6 7 Value 24 12 20 11 19 24 16 (a) Compute MSE using the most recent value as the forecast for the next period. If required, round your answer to one decimal place. What is the forecast for month 8? If required, round your answer to one decimal place. Do not round intermediate calculation. (b) Compute MSE using the average of all the data available as the forecast for the next period. If required, round your answer to one decimal place. Do not round intermediate calculation. What is the forecast for month 8? If required, round your answer to one decimal place. (c) Which method appears to provide the better forecast? All Data average or Naiive
Consider the following time series data. Quarter Year 1 Year 2 Year 3 1 4 6 7 2 2 3 6 3 3 5 6 4 5 7 8 Choose the correct time series plot. (i) (ii) (iii) (iv) - Plot (iii) What type of pattern exists in the data?- Horizontal Pattern with Seasonality Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data. Qtr1 = 1 if Quarter 1, 0 otherwise; Qtr2 = 1 if Quarter 2, 0 otherwise; Qtr3 = 1 if Quarter 3, 0 otherwise. If required, round your answers to three decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300)Value = fill in the blank 3 + fill in the blank 4 Qtr1 + fill in the blank 5 Qtr2 + fill in the blank 6 Qtr3 + fill in the blank 7 t Compute the quarterly forecasts for next year. If…
Consider the following time series data: Month 1 2 3 4 5 6 7 Value 23 14 19 12 20 24 15 (a) Compute MSE using the most recent value as the forecast for the next period. If required, round your answer to one decimal place. What is the forecast for month 8? If required, round your answer to one decimal place. Do not round intermediate calculation. (b) Compute MSE using the average of all the data available as the forecast for the next period. If required, round your answer to one decimal place. Do not round intermediate calculation. What is the forecast for month 8? If required, round your answer to one decimal place. (c) Which method appears to provide the better forecast? - Select your answer -NaïveAll data averageItem 5
Consider the following time series data: Month 1 2 3 4 5 6 7 Value 23 14 19 12 20 24 15 (a) Compute MSE using the most recent value as the forecast for the next period. If required, round your answer to one decimal place. What is the forecast for month 8? If required, round your answer to one decimal place. Do not round intermediate calculation. (b) Compute MSE using the average of all the data available as the forecast for the next period. If required, round your answer to one decimal place. Do not round intermediate calculation. What is the forecast for month 8? If required, round your answer to one decimal place.
Consider the following time series date Week: 1 2 3 4 5 6 Value: 16 15 17 13 16 13 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 Value Forecast 1 16 2 15 3 17 4 13 # 5 16 # 6 13 # MSE: The forecast for week 7: Use alpha=0.2 to compute the exponential smoothing values fro time series. Compute MSE and a forecast for week 7 if required, round your answers to two decimal places Week value forecast 1 16 2 15 16 3 17 15.8 4 13 16.04 5 16 15.43 6 13 15.55 MSE: 3.69 The forecast for week 7:
Consider the following time series data. Quarter Year 1 Year 2 Year 3 1 4 6 7 2 2 3 6 3 3 5 6 4 5 7 8 Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data. Qtr1 = 1 if Quarter 1, 0 otherwise; Qtr2 = 1 if Quarter 2, 0 otherwise; Qtr3 = 1 if Quarter 3, 0 otherwise. If required, round your answers to three decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300)Value = + Qtr1 + Qtr2 + Qtr3 + t Compute the quarterly forecasts for next year. If required, round your answers to two decimal places.Quarter 1 forecast = Quarter 2 forecast = Quarter 3 forecast = Quarter 4 forecast =

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	Use trial and error to find a value of the exponential smoothing coefficient α that results in a smaller MSE than what you calculated for α = 0.20. Use a two-decimal digit precision.
	alpha:

Week	1	2	3	4	5	6
Value	17	14	17	11	16	15