Dave’s Donuts offers 14 flavors of donuts (consider the supply of each flavor as being unlimited). The “grab bag” box consists of flavors randomly selected to be in the box, each flavor equally likely for each one of the dozen donuts. What is the probability that at most three flavors are in the grab bag box of a dozen?
In Data Science from Scratch, a book introducing data science using Python, Joel Grus said the following about R (pg. 302):
Although you can totally get away with not learning R, a lot of data scientists and data science projects use it, so it’s worth getting familiar with it.
In part, this is so that you can understand people’s R-based blog posts and examples and code; in part, this is to help you better appreciate the (comparatively) clean elegance of Python; and in part, this is to help you be a more informed participant in the never-ending “R versus Python” flamewars.
At the University of Utah, I teach the R lab that accompanies MATH 3070, “Applied Statistics I.”” None of my students are presumed to have any programming experience, and they never hesitate to remind me of that fact, especially when they are starting out. When I create assignments and pick problems, I often can write a one- or three-line solution in thirty seconds that students will sometimes spend four hours trying to solve. They then see my solution and slap their foreheads at its simplicity. I can be tricky with my solutions. For example, suppose you wish to find the sample proportion for a certain property. A common approach (or at least the one used in the textbook our course uses, Using R for Introductory Statistics by John Verzani) looks like this:
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