On the other hand, it can be used to understand the price elasticity of a particular company’s products. This allows the managers to better understand the change in the sales with a change in price and decide on the best price that will maximize the revenue. At the same time, a finance manager can use linear regression to predict the trend in the prices of a particular stock. Based on the historical data in chronological order, a time series plot can be prepared for several years (Keller, pp. 7). Based on it, the pattern can be predicted and best decisions can be made to for effective investments (Keller, pp. 7). At the same time, sales projections can be made based on historical values. Figure 1 shows the situation.
In the business environment, managers come across various complex situations where relationships are not precise and clear (Victor, pp. 1). In these complex situations, linear regression can be used to develop relationships and identify the necessary patterns and hence, allow the managers to reach good decisions in a limited amount of time (Victor, pp. 1). An example of this situation could be the effect of the advertising on the sales figures for a specific product (Victor, pp. 1).
The equation for linear regression is y = β0 + β1x where β1 = slope = change in mean value of Y for a unit change in x. In this case β1 = cov(x,y) / var (x).
Descriptive statistics summarize a group of data into more presentable and understandable manner. It is used to describe the collection of data and clearly identify the trends and patterns in them. The measures of central tendency and dispersion describe that data with the use of numbers whereas the graphic representations provide a clear picture (Keller, pp. 93, 101, 102, 105).
The mean is considered the single most well-accepted and important measure in the business world. It finds immense applications in the field of finance (Keller, pp.6). The relevant managers make regular decisions based on the mean returns of the portfolio investment.
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