Opportunity Cost in Data Mining

Data Mining

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Opportunity Cost in Data Mining

Opportunity cost is defined as the price of any action measured regarding the worth of next greatest substitute foregone. It entails sacrifice related to the second best choice available to a group or an individual who has selected from a number of choices. As a choice, the opportunity cost is dependent on activities that are not taken up. Opportunity cost is especially beneficial when calculating the margin and price of choice. From Shmueli, Patel & Bruce (2016), the opportunity cost is not usually regarded by auditors, monetary statements only consist of actual outlays or explicit cost, and they ought to be considered by managers. Most enterprise owners consider opportunity cost, especially when presented with presented with two or more possible actions. Smaller enterprises reason in opportunity cost when they calculate their operating expenses to estimate the price of a job or provide a bid.

Prediction vs. Explanation

        Author Shmueli, Patel & Bruce (2016), points out two main uses of multiple regression: causal and prediction analysis. Under the prediction study of multiple regression, the objective is to create a formula that can project the dependent variable using observed values of the independent variables. Causal analysis on the other hand independent variables are viewed as the basis for the dependent variable. This comparison aims at establishing whether a specific independent variable impacts the dependent variables and to project the extent of the effect.

           Despite the fact that multiple regression is used to draw predictions and casual inferences, there are significant disparities in the application of methodology in the two types of application. The differences are

Omitted Variables: Under causal inferences, the chief objective is to obtain unbiased estimations of the regression coefficients.  For nonexperimental data, the significant obstacle to the objective is the bias of omitted variables (Shmueli, Patel & Bruce, 2016). Specifically, variables that impact dependent variables and are related with variables in the model. The omission of such data can lead to wrong conclusions. On the other hand, for predictive models omitted variables are much less of an issue since the main objective is obtaining an optimal prediction based on linear combination of available variables.