I recently learned that Google requires affiliate links to be “nofollow” links. Compared to the often default “follow” links, “nofollow” links essentially remove the website endorsement typically implied by websites linking to each other.

I go over methods for data imputation for training machine learning models. These techniques are inappropriate for hypothesis testing because they do not account for the uncertainty in the imputed data. However, if you are training a neural network on thousands of rows of data, and have missingness, these methods could be a good solution.

Much of what we do in statistics requires that the data we are using be normally distributed. This prolific assumption requires that we either visually inspect the data or use a hypothesis test. While hypothesis tests like the Shapiro-Wilk test offer a clear-cut decision, it is sometimes preferred to simply visually inspect the data.

A guide to performing many different types of t-tests including: one-sample, two-sample assuming equal variance, two-sample assuming unequal variance, and two-sample dependent measures.

I became interested in making functions that describe data being fed through a pipe stream without changing it.

Naive Bayes is a computationally simple, but incredibly effective method for classification. In this tutorial, I will show you how to run this model and determine the classification accuracy of the model.

In this tutorial, you will learn how to create a random forest classification model and how to assess its performance.

We may encounter situations where we can not store the data set and calculations required for regression models in RAM. This article presents a technique where we can estimate regression coefficients by sampling from a data set and running smaller regression calculations.

One of the most powerful aspects of R is that it has a diverse set of random number generators. We can use these R tools to create methods of obscuring a message in what appears to be meaningless strings of text (cryptography).

A while statement will run as long as the conditional statement it is given evaluates as true. Naturally, the basic setup then is to make a conditional statement that will change over time and eventually evaluate as false when it no longer needs to keep running.

One of the most initially confusing aspects of R is using loops to repeat a chunk of code. I hope you will find this tutorial helpful and start to understand the power loops give to our code.

There will be instances where you will want to create a vector that contains a repeated value or special sequence.

To create a variable we put the variable name we want on the left, add a left pointing arrow (“<“ followed by “-”), and then specify the number(s) we want to assign.

Congratulations on starting your R programming journey!