Year Month Sales 1 January 242 1. Develop a time series plot. Comment on the underlying February 235 pattern in the time series. March 232 2. An analysis of the seasonality of the data. Indicate the April 178 seasonal indexes for each month, and comment on high and May 184 low seasonal sales months. Do the seasonal indexes make June 140 intuitive sense? Discuss. July 145 3. Deseasonalize the time series. Does there appear to be any August 152 trend. Septembe r 110 4. Using the time series decomposition method, forecast sales October 130 for January through december of the fourth year. Novembe r 242 5. Using the dummy variable regression approach, forecastsales for January through December of the fourth year. Decembe r 235 6. Provide summary tables232your calcualtions and any graphs in the appendix of your report. of 2 January February 178 March 184 Assume that January sales for the fourth year turn out to be April 140 $295,000. What was your forecast error? May 145 June 152 If this error is too large Karen may be puzzled about the difference between your forecast and actual sales value. July 110 What can you do to resolve her uncertainty in the forecasting procedure. August 130 Septembe r 152 October 206 Novembe r 263 Decembe r 238 3 January 247 February 193 March 193 April 149 May 157 June 161 July 122 August 130 Septembe r 167 October 230 Novembe r 282 Decembe r 255