Inferential Statistics, however, helps in understanding how the various variables are related and if the relationship that pertains amongst them is significant or not. This includes watching over the mean, mode or median along with the averages and graphical plots for the vast information that the data frame entails. It has made us, as analysts or as curious folks look at the highly complex data sets and get to know a lot about it in a single glance. Descriptive statistics have helped to make the descriptions of our data sets very easy. We start analyzing data while simultaneously deriving statistical reports, Descriptive and Inferential being the two forms for the same. To kick off with understanding the intricate details of this concept, let’s start from the very beginning. While exploring the data, one of statistical test we can perform between churn and internet services is chi-square - a test of the relationship between two variables - to know if internet services could be one of the strong predictors of churn. One of the variables we have got in our data is a binary variable (two categories 0,1) which indicates whether the customer has internet services or not. Let’s think of a scenario - we are looking to build a predictive model which will predictive the probability of a telecom customer attrition.
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