data are Most of the phenomena in economics and business show seasonal patterns. is no seasonal variation. expressed annually there However, monthly or quarterly data frequently exhibit strong seasonal movements and considerable interest attaches to devising a pattern of average seasonal variation. For example, if we observe the sales of a bookseller we find that for the quarter July-September (when most of the students purchase books), sales are maximum. If we know by how much the sales of this quarter are usually above or below the previous quarter for seasonal reasons, we shall be able to answer a very basic question, namely, was this due to an underlying upward tendency or simply because this quarter is usually seasonally higher than the previous quarter. In order to analyse seasonal variation, it is necessary to assume that the seasonal pattern is superimposed on a series of values and is independent of these in the sense that the same pattern is superimposed irrespective of the level of the series, i. e., the June quarter always contributes so much more or so much less to the series. Before attempting to measure seasonal variation certain preliminary decisions must be made. For example, it is necessary to decide whether weekly, quarterly or monthly indexes are required. This will be decided in the light of the nature of the problem and the type of data available seasonal adjustments help avoid misinterpretation. ling Convert the following annual trend equation on a monthly basis : Y= 10.6 + 0.8X +0.64 x2 onvert 'd' by