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Time Series and Forcasting

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Time Series and Forecasting

Learning Team A

Quantitative Reasoning for Business/501

August 23, 2011
Dr. Champion

Time Series and Forecasting

The purpose of this paper is for statisticians from the accountant department of Norton Company to compute the quarterly seasonal index for the years of 2003 through 2006 by using the ratio-to-moving-average method. In addition, the accountants will deseasonalize the data and determine the trend equation. Furthermore, the statisticians will estimate Norton Company’s seasonally adjusted sale for the four quarters of 2007. The quarterly sales for the Norton Company were given in millions of dollars for four years. Therefore, the recorded quarterly sales for the Norton Company were referenced and included 16 quarters total. A scatter plot was graphed showing the recorded sales for the 16 quarters. The following equation was obtained from the historical data: y = 0.461x + 4.2 with the R² of 0.1695 and a R of 0.412. R is the coefficient of correlation, which provides the strength and direction of a linear relationship. R2 is the coefficient of determination, which measures the amount of variation in y that can be explained by the variation in x for example: 0 ≤ R2 ≤ 1. With this unadjusted regression equation and R² we can see that there is seasonality. Accordingly, a higher R² is required to prove that our equation is accurate and good for forecasting. The first step is to find the moving total. The moving total presents a summative of data from the consistency of quarterly sales during the number of periods. Then the moving average is calculated to smooth the data series, which make it easier to spot trends. After that, the center-moving-average was computed to close the gaps. The center-moving-average entails the statisticians to smooth away any seasonal and irregular variation.

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