Consider the following time series data: Week Value 1 18 2 13 3 16 4 11...

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Consider the following time series data: Week Value 1 18 2 13 3 16 4 11 5 17 1) Construct a time series plot. What type of trend exists in the data? 2) Develop the three-week moving average forecasts for this time series: 1 mt = Σ--∞ ajXt-j, where a;=, j = 1,2,3, zero else. 88 aj 3 Compute the mean squared error. Compute a forecast for week 7. 3) Develop the three-week moving average forecasts for this time series: m₁ = Σ;=-∞ ª¡Xt-j, where (a₁, a₂, a3) = (), zero else. Compute the mean squared error. Compute a forecast for week 7. 6 14 4) Use a = 0.2 to compute the exponential smoothing forecasts for the time series. Compute the mean squared error. Compute a forecast for week 7. 5) For the above data calculate BX₁, B2X, VX, V₂Xt, and V2X₁. 6) For the above data calculate Ŷ(h), and p(h). 7) Try to fit a linear regression model for the above data. What can you conclude? Consider the following time series data: Week Value 1 18 2 13 3 16 4 11 5 17 1) Construct a time series plot. What type of trend exists in the data? 2) Develop the three-week moving average forecasts for this time series: 1 mt = Σ--∞ ajXt-j, where a;=, j = 1,2,3, zero else. 88 aj 3 Compute the mean squared error. Compute a forecast for week 7. 3) Develop the three-week moving average forecasts for this time series: m₁ = Σ;=-∞ ª¡Xt-j, where (a₁, a₂, a3) = (), zero else. Compute the mean squared error. Compute a forecast for week 7. 6 14 4) Use a = 0.2 to compute the exponential smoothing forecasts for the time series. Compute the mean squared error. Compute a forecast for week 7. 5) For the above data calculate BX₁, B2X, VX, V₂Xt, and V2X₁. 6) For the above data calculate Ŷ(h), and p(h). 7) Try to fit a linear regression model for the above data. What can you conclude?

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1 The time series plot shows a fluctuating trend with no clear increasing or decreasi... View the full answer