Seasonal Variations, Business Mathematics and Statistics B Com Notes | EduRev

Business Mathematics and Statistics

Created by: Arshit Thakur

B Com : Seasonal Variations, Business Mathematics and Statistics B Com Notes | EduRev

The document Seasonal Variations, Business Mathematics and Statistics B Com Notes | EduRev is a part of the B Com Course Business Mathematics and Statistics.
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Seasonal Variations : The two principal factors liable for seasonal changes are the climate or weather and customs. Since, the growth of all vegetation depends upon temperature and moisture, agricultural activity is confined largely to warm weather in the temperate zones and to the rainy or post-rainy season in the torried zone (tropical countries or sub-tropical countries like India). Winter and dry season make farming a highly seasonal business. This high irregularity of month to month agricultural production determines largely all harvesting, marketing, canning, preserving, storing, financing, and pricing of farm products. Manufacturers, bankers and merchants who deal with farmers find their business taking on the same seasonal pattern which characterise the agriculture of their area.

The second cause of seasonal variation is custom, education or tradition. Such traditional days as Dewali, Christmas. Id etc., product marked variations in business activity, travel, sales, gifts, finance, accident, and vacationing.

The successful operation of any business requires that its seasonal variations be known, measured and exploited fully. Frequently, the purchase of seasonal item is made from six months to a year in advance. Departments with opposite seasonal changes are frequently combined in the same firm to avoid dull seasons and to keep sales or production up during the entire year. Seasonal variations are measured as a percentage of the trend rather than in absolute quantities. The seasonal index for any month (week, quarter etc.) may be defined as the ratio of the normally expected value (excluding the business cycle and erratic movements) to the corresponding trend value. When cyclical movement and erratic fluctuations are absent in a lime series, such a series is called normal. Normal values thus are consisting of trend and seasonal components. Thus when normal values are divided by the corresponding trend values, we obtain seasonal component of time series.

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