Regression Basics for Business Analysis, Essay Example

Business makes considerable use of linear regression models and forecasting. It is most commonly used in Marketing Departments and a predictor for Sales forecasting. For example: A Marketing Department conducting research for its’ products, considers there will be a change in GDP by a certain unidentified %.  By use of linear regression you can take different % values and generate a sales forecast for different products. Simple regression models are therefore a facilitator for the development of more complex objective planning and budgets.  Within a simple regression model, the major components are:

• R Squared : how well future sales are predicted;
• The intecept : how the change in GDP fluctuates;
• GDP co-efficient: the correlation between GDP and sales;

The diagram to the right provides an example of these figures.  (Nguyen)

It is often useful to examine the relationship between two variables in an equation. This is  known as the co-variance.  The mathematics illustrate both the direction of the variance and its strength. In the example provided above we examined GDP and Sales.  Hence if sales goes up and GDP goes down this is considered a positive variance. The co-variance would be the negative result of the equation.

Once you have computed the necessary information by means of the mathematical model it is then possible to plot the results in graphic form and demonstrate a sales forecast for the business.  The regression line tends to provide a best fit between the various plots of data, essentially a smoothing between the results plotted. The following graph provides a brief illustration of this.

Example Sales Forecast

Source:  (Nguyen)

Works Cited

Nguyen, Joseph. Regression Basics For Business Analysis . 3 8 2010. 3 8 2010 <http://www.investopedia.com/articles/financial-theory/09/regression-analysis-basics-business.asp>.