Regression Analysis

From the free, on-line encyclopedia, Wikipedia:

In statistics, regression analysis is a collective name for techniques for the modeling and analysis of numerical data consisting of values of a dependent variable (response variable) and of one or more independent variables (explanatory variables). The dependent variable in the regression equation is modeled as a function of the independent variables, corresponding parameters ("constants"), and an error term. The error term is treated as a random variable. It represents unexplained variation in the dependent variable. The parameters are estimated so as to give a "best fit" of the data. Most commonly the best fit is evaluated by using the least squares method, but other criteria have also been used.

Regression analysis offers another powerful set of tools we can use to understand our businesses. It can tell us what factors best influence buyer behavior. That is, if we know buyers' age, income, gender, and so forth, how much do all of these factors affect the propensity of a customer to buy and which factor is most important, which second, and so forth.

Further, we can look at various factors of our business, revenue, expenses, mean receivable days, etc., and find out which has had the greatest impact on profit or sales growth.

However it is very complicated and arcane to the normal person, and the "regressor" must be trained in its usage.

If you are interested in this, read the whole article and follow its links and references. And post a comment. I'd like to know what you think.

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