... Year January 242 263 282 February 235 238 255 March 232 247 265 April 178 193 205 May 184 193 210 June 140 149 160 July 145 157 166 August 152 161 174 September 110 122 126 October 130 130 148 November 152 167 173 December 206 230 235 The statistical Summary of the data is shown below: Year January February March April May June July August September October November December Total Sales 1 242 235 232 178 184 140 145 152 110 130 152 206 2106 2 263 238 247 193 193 149 157 161 122 130 167 230 2250 3 282 255 265 205 210 160 166 174 126 148 173 235 2399 Total: 787 728 744 576 587 449 468 487 358 408 492 671 6755 Mean: 262.3333333 242.66667 248 192 195.6667 149.6667 156 162.3333 119.33333 136 164 223.6667 2251.666667 Variance: 400.3333333 116.33333 273 183 174.3333 100.3333 111 122.3333 69.333333 108 117 240.3333 21464.33333 StDev: 20.0083316 10.785793 16.52271 13.52775 13.20353 10.01665 10.53565 11.06044 8.326664 10.3923 10.81665 15.50269 146.5071102 Analyze the Data There are two main goals of time series analysis: (a) identifying the nature of the phenomenon represented by the sequence of observations, and (b) forecasting (predicting future values of the time series variable). Both of these goals require that the pattern of observed time series data is identified and more or less formally described. Once the pattern is established, we can interpret and integrate it with other data (i.e., use it in our theory of the investigated phenomenon, e.g., seasonal commodity prices). Regardless of the depth of our understanding and the validity of our interpretation (theory) of the phenomenon, we can extrapolate the identified pattern to predict future events. Time Series Graph To discover the characteristic of the time series, the visual inspection of the graph is the first step in any time series analysis ...

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