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Showing posts from October, 2017

What caused the Challenger disaster?

The motivation for this blog is to examine the reasons behind the explosion of the USA Space Shuttle Challenger on 28 January, 1986. The night before the launch a decision had to be made regarding launch safety and engineers recommended that the launch be postponed in the event the temperature at launch was below freezing as this adversely impacted the integrity of O-rings, a key component holding in field joints. The engineers advice was ignored and disaster ensued. Let's dive in!

Regression in R

My latest publicly available R notebook created in IBM's Data Science Experience is here!  This notebook provides a tutorial on:

This notebook covers:
Fitting and interpreting linear models;Evaluating model assumptions; andSelecting among competing models.I hope you enjoy this notebook.  Please feel free to share and let me know your thoughts.

My latest notebook: Regression in R https://t.co/HDYFzTAFPr#rstats#DataScience#ibmaot#Statistics#Stats#dsx#Bluemix h/t @kabacoffpic.twitter.com/LxKc9HkBC0 — Venky Rao (@VRaoRao) October 15, 2017

Coefficient of Alienation

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If you thought the coefficient of alienation referred to the hostility I receive from my family as I update my blog on a Saturday afternoon, I would not fault you too much.  However, this is a blog about predictive analytics which is based on Statistics.  So let's keep that in mind as we understand what the "Coefficient of Alienation" means.

Apart from being one of the coolest sounding Statistical terms, the Coefficient of Alienation measures the proportion of variation in the outcome not “explained” by the variables on the right-hand side of a simple linear regression (ordinary least squares) equation.

The Coefficient of Alienation is also known as the Coefficient of Non-Determination since the formula for calculating it is:


where:









And now before my personal (and non-Statistical) Coefficient of Alienation reaches the point of no return, I will bring this post to an end.