R Stats and Google Colab - Part 2 - Linear Regression

 If you read my other post on using cloud notebooks, you'll remember that I mentioned Google Colaboratory.  Google Colab is a great resource for those who are used to performing data science in Jupyter Notebooks or in RStudio.  It is a great place to collaborate with other coders as well.  The notebooks can be imported, exported and shared.  

As an R lover, I have built the first part of two tutorials on linear regression.  It's for beginners, going back to my roots as a statistics instructor.  This first tutorial is on Exploratory Data Analysis (EDAs).  

I've posted it in GitHub, and the notebook can be downloaded.  It's commented out for someone who is new to R, stats, or Colab.  Because the dataset is native to R, it's plug-and-play, therefore the code can easily be run in any R environment.

Here's the link: GitHub EDAs

Now, I have built the second half - Linear Regression and Multiple Linear Regression.  You can find it here on GitHub Linear Regression or also on Kaggle Linear Regression in Docker

Again, because its using a native R dataset, it is plug-n-play and will run in any R environment.  It is a beginner level exercise (Statistics and Coding-wise).  I hope you enjoy!