Friday, October 18, 2019

No topic Assignment Example | Topics and Well Written Essays - 500 words - 12

No topic - Assignment Example The linear regression equation helps in forecasting the value of sales in the next year. This will be done as follows; sales for the seventh year = (1109.1*7) + 1408 = 2517.1. The predicted sales for the seventh year is $ 2,517. The above prediction is too general to provide enough information for the production planning. The data that is divided into the different seasons in the year is more informative to the production planning especially for the Riverside Corporation that deals in highly seasonal products (Anderson, 677). Anderson adds that time series help in showing the overall trend of data for specified time intervals (692). Similar scatter diagrams help in predicting the sales for the next year per every two months as follows; The above analysis shows that the initial value of predicted sales was $ 2517 and was based on the annual total sales of the year. When data has been broken down into two months each year, the prediction takes a different direction (Anderson, 2012). For the next year i.e. seventh year, the predicted sales for the first two months is $ 2815. Sales for the second, third, fourth, fifth and sixth two months are $ 1659, $1240, 701, 797 and 1960 respectively. This information is more useful for production planning than the prediction made using the annual totals. This is because the product in question is seasonal and therefore its demand varies depending on different seasons of the year. Time series analysis is used in predicting the future values of a variable by the use of response history (Anderson, 682). This is referred to as autoregressive dynamics (Anderson, 680). The basic application is in the application of the linear regression models used above. The models give an equation where values are substituted to obtain the intended prediction. According to Anderson, â€Å"time series captures the various trends that given data assume over a certain period of time† (662).

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