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Consider the Monthly Time Series Shown in the Table A Use the Method of Least Squares to Fit the

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Consider the monthly time series shown in the table.  Month tY January 1185 February 2192 March 3189 April 4201 May 5195 June 6199 July 7206 August 8203 September 9208 October 10209 November 11218 December 12216\begin{array} { l | c | c } \hline \text { Month } & \mathrm { t } & \mathrm { Y } \\\hline \text { January } & 1 & 185 \\\hline \text { February } & 2 & 192 \\\hline \text { March } & 3 & 189 \\\hline \text { April } & 4 & 201 \\\hline \text { May } & 5 & 195 \\\hline \text { June } & 6 & 199 \\\hline \text { July } & 7 & 206 \\\hline \text { August } & 8 & 203 \\\hline \text { September } & 9 & 208 \\\hline \text { October } & 10 & 209 \\\hline \text { November } & 11 & 218 \\\hline \text { December } & 12 & 216 \\\hline\end{array} a. Use the method of least squares to fit the mo E(Yt)=β0+β1\mathrm { E } \left( \mathrm { Y } _ { \mathrm { t } } \right) = \beta _ { 0 } + \beta _ { 1 } t to the data. Write the prediction equation.
b. Use the prediction equation to obtain forecasts for the next two months.
c. Find 95% forecast intervals for the next two months.

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