What is Correlation?

Correlation is a statistical measure that can show whether and how strongly pairs of variables are related. For example, height and weight are related; taller people tend to be heavier than shorter people. The relationship isn’t perfect. People of the same height vary in weight, and you can easily think of two people you know where the shorter one is heavier than the taller one. Nonetheless, the average weight of people 5’5” is less than the average weight of people 5’6”, and their average weight is less than that of people 5’7”, etc. Correlation does not imply causation, rather it implies an association between two variables. The strength of a correlation can be indicated by the correlation coefficient.

The correlation coefficient is a measure of how closely two variables move in relation to one another. If one variable goes up by a certain amount, the correlation coefficient indicates which way the other variable moves and by how much. A correlation coefficient is in the range of -1 to zero to +1. When two variables, X and Y, have a correlation coefficient approaching -1, if variable X goes up by one unit, variable Y will go down by one unit. If their correlation coefficient approaches +1, and X goes up by one unit, Y will also go up one unit. A correlation coefficient of zero means movement of the X and Y variables is unrelated.

Was the above useful? Please share with others on social media.

If you want to look for more information, check some free online courses available at   coursera.orgedx.org or udemy.com.

Recommended reading list:

 

Data Science from Scratch: First Principles with Python

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.

If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.

Get a crash course in Python
Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science
Collect, explore, clean, munge, and manipulate data
Dive into the fundamentals of machine learning
Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering
Explore recommender systems, natural language processing, network analysis, MapReduce, and databases