This is the fourth entry in our regression analysis and modeling series. In this tutorial, we continue the analysis discussion we started earlier and leverage an advanced technique –regression stability test - to help us detect deficiencies in the selected model, and thus the reliability of the forecast.
Again, we will use a sample data set gathered from 20 different sales persons. The regression model attempts to explain and predict weekly sales for each salesperson (dependent variable) using two explanatory variables: intelligence (IQ) and extroversion.
Similar to what we did in an earlier tutorial, we organize our sample data by placing the value of each variable in a separate column and each observation in a separate row.
In this example, we have 20 observations and two independent (explanatory) variables. The response or dependent variable is the weekly sales.
Next, we introduce the “mask”. The “mask” is a Boolean array (0 or 1), which chooses which variable is included (or excluded) from the analysis.
Let’s use the results from the 3rd entry in this tutorial series, and set the mask entry for “Intelligence” to be 0 and the extroversion to be 1.