One of the purposes of business analytics is to understand the relationship between potential causes and effects of interests. Basic correlation analysis fails to do so and doesn't consider the joint effects of different rivals. Hence, the necessity to use reliable statistical models that measure those relationship in a rigorous and quantitative way.

When approaching such a model, we can procedd as follows:

1. We assess whether the resulting model is accurate
2. If the resulting model is accurate, what are the significant factors
3. Can we rank those factors by order of importance? and assess if they are positve for negative to the business
4. We need to find a visual way to report the relationship between the most important drivers and the outcome we are interested in

In this week we are going to investigate two realistic examples using linear regressions:

1. How to model credit scores in function of different financial and social demographics factor [CreditScore] and,
2. how to understand what's driving the employees attrition of a company [HRAnalytics]