Consider the following time series data. Quarter Year 1 Year 2 1 2 2 3 3 4 Year Quarter 1 2 3 4 1 2 3 4 1 (a) Deseasonalize the time series using the adjusted seasonal indexes given below. (Round your answers to three decimal places.) Time Series Value Adjusted Seasonal Index 2 3 4 4 2 3 4 4 2 3 4 6 3 5 8 7 6 6 6 9 3 5 8 Year 3 7 11.6805 x 12.1378 X 6 9.566 8.910 6 9 X x 10.374 X 13,832 x 1.230 0.734 0.870 1.165 1.230 0.734 0.870 1.165 1.230 0.734 0.870 1.165 Deseasonalized Value 3.252 2.725 3.448 3.433 4.878 4.087 5.747 6.867 5.691 8.174 6.897 7.725 ✓ ✓ ✓ ✓ ✓ ✓ ✓ (b) Using statistical software, compute the linear trend regression equation for the deseasonalized data. (Let t= 1 denote the time series value in quarter 1 of year 1; t = 2 denote the time series value in quarter 2 of year 1;... and t= 12 denote the time series value in quarter 4 of year 3. Round your numerical values to three decimal places.) T₁ = 4.1 +0.457tx ✓ (c) Compute the deseasonalized quarterly trend forecast for year 4. (Round your answers to three decimal places.) forecast for quarter 1 10.7659 x forecast for quarter 2 11.2232 x forecast for quarter 3 forecast for quarter 4 ✓ (d) Use the seasonal indexes to adjust the deseasonalized trend forecasts computed in part (c). (Round your answers to three decimal places.) forecast for quarter 1 forecast for quarter 2 forecast for quarter 3 forecast for quarter 4

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