Week 6-7: Learning More About Statistical Analysis

Hello everyone! Thank you once again for tuning in to my project. I was on vacation to San Diego to visit some college campuses for the past week. 

As promised, I have been learning about statistical analysis for the past week or so. The topics that I mainly covered included Linear Regression, Descriptive Stats, and Inferential Stats. Here’s a quick rundown: Linear Regression is basically a statistical model used in Machine Learning to predict the outcome of events which is going to be extremely useful in my project as my predictive model will have to predict how likely a customer will churn based off of his/her characteristics (the dependent variables). The way we will test how significant of an impact each customer’s characteristic has on their churn rate will be using the P value. The P-value will be used in my project to conclude if there is a relationship between the customer’s churn rate and one of their specific characteristics (ie. gender, age, tenure, etc). 

Other useful thing that I learned that I will be implementing in my project are Descriptive Statistics and Inferential Statistics. Descrpitive Statistics describes and summarizes the basic characterisitics of my data set, presented in a summary that shows the data sample and its measurements. On the other hand, Inferential statistics is used to make inferences (or hypotheses) about a certain population. This will be arguably much more useful going forward as I will be doing many Hypothesis testing about the customers in my data set. However, there will always be an uncertainty when making certain estimations about how much a characteristic affects a customer’s churn rate, which is a limiting factor in the Statistical Analysis process. 

References:

• “Data Science – Linear Regression.” Data Science Linear Regression, Https://Www.W3schools.Com/Datascience/Ds_linear_regression.Asp.
• “Statistics Tutorial.” Statistics Tutorial, Https://Www.W3schools.Com/Statistics/.