Forecast

honest Ms. Jones In order to obtain the approximate for the 5th year we had to gather and analyze the data of the four previous years in your company. The sheer (data behaving with the same frequence over the years) that was found was the following The solution months of the year are the ones with higher gross revenue. As the months go by, gross gross revenue continue change magnitude until December, whither gross revenue come back up again. Now, let me explain how we were able to sire to this conclusion. First, we calculated the reasonable demand by adding up both the sales of on the whole four years and dividing them by the quash of months (48).Then, we came up with the ratio by dividing the sales of each period by the add up demand. The seasonal index is then obtained by carryting the average of the same month ratios of alone four years. For example, the average of all the 4 January ratios. The seasonal index is an average that can be used to equalise an actual observation relative to what it would be if we there were no seasonal variation. We land to the seasonal forecast by dividing the sales by the seasonal index. Then we get the edit ocellus by adding the solicit plus the x-variable and multiplying that by each period.The trend forecast is what will show you the unwavering trend of the years. That is obtained by multiplying the trend line times the seasonal index. Heres a snapshot of the trend of the what the fifth year would look like And here is another graph showing the trend of the four previous years As you can tell, the sales behavior repeats itself end-to-end the years. This trend seems to be very consistent. However, I must warn you that the p-value (percentage defective) in the compend output is significantly higher than . 06, (it is a. 404056) and this inwardness this forecast is not very reliable.I also calculated the percentage shifts the domineering percentage error (MAPE) is 3. 85%. This error was calculated b y dividing the absolute error (which we got by subtracting the trend forecast from the sales and using the absolute value of that), by the sales, and then getting the percentage of all the absolute percentage errors. I expect this helps you understand the trend of your sales passim a year. The most important affair for you to identify is the months where you are having higher sales the possible reasons why those sales slack as the years comes to an end.