Perfecting Linear Regression Predictions - Assignment Example

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From the paper "Perfecting Linear Regression Predictions" it is clear that too many variables may not give accurate prediction either because some of the events may not happen in future. To improve the results, more data points should be used in conjunction with a fair number of multiple variables…

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Perfecting Linear Regression Predictions

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Subject: Marketing
Type: Assignment
Level: Ph.D.
Pages: 1 (250 words)
Downloads: 2
Author: schusterflorian

Extract of sample "Perfecting Linear Regression Predictions"

Using regression analysis for predicting quarterly purchases of building materials may not yield accurate results because of many factors such as the rise of unpredictable situations, the duration in which data points are taken, and the number of variables used for regression analysis.

First, regression analysis uses data from past events and therefore may not be accurate if an unaccounted event occurs. For instance, a natural catastrophe such as an earthquake may stall all building constructions in a given area forcing customers to stop buying. If linear regression uses previous data where catastrophe was absent, then its outcome will be inaccurate. Such errors can be avoided by adding as many variables as feasibly possible to the regression model used (Montgomery, 2011).

The duration in which regression data points were taken determines the accuracy of the regression results. More data points are more likely to yield more accurate results. For instance, monthly sales spanning about five years can provide accurate regression results of sales as opposed to quarterly sales of the past year. Lastly, regression modelling is only as accurate as the variables used (Seber et al. 2003). Too few variables may not give accurate predictions because they may not give an accurate history. Read More

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