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Consider the Quarterly Production Data (In Thousands of Units) for the XYZ

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Consider the quarterly production data (in thousands of units) for the XYZ manufacturing company below. The normalized (adjusted) seasonal factors are winter = .9982, spring = .9263, summer = 1.139, and fall = .9365.
Consider the quarterly production data (in thousands of units) for the XYZ manufacturing company below. The normalized (adjusted) seasonal factors are winter = .9982, spring = .9263, summer = 1.139, and fall = .9365.    Based on the following deseasonalized observations (d<sub>t</sub>), a trend line was estimated. The linear regression trend equation is tr<sub>t</sub> = 10.1 + 1.91t. Use the forecasting equation   <sub>t</sub> = tr<sub>1</sub> × sn<sub>t</sub> and calculate the forecasted demand for the fall quarter of 1998 and summer quarter of 2000. Based on the following deseasonalized observations (dt), a trend line was estimated. The linear regression trend equation is trt = 10.1 + 1.91t.
Use the forecasting equation Consider the quarterly production data (in thousands of units) for the XYZ manufacturing company below. The normalized (adjusted) seasonal factors are winter = .9982, spring = .9263, summer = 1.139, and fall = .9365.    Based on the following deseasonalized observations (d<sub>t</sub>), a trend line was estimated. The linear regression trend equation is tr<sub>t</sub> = 10.1 + 1.91t. Use the forecasting equation   <sub>t</sub> = tr<sub>1</sub> × sn<sub>t</sub> and calculate the forecasted demand for the fall quarter of 1998 and summer quarter of 2000. t = tr1 × snt and calculate the forecasted demand for the fall quarter of 1998 and summer quarter of 2000.


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