Recently, /u/mikeeg555 created this post
on the statistics subreddit with their results from a simulation of
10,000,000 games of this instance of Snakes and Ladders. This is the sort of information that's good to
show in an undergrad or senior secondary level classroom as a
highlight of the sort of insights you can get from the kind of
simulation that anyone can program.
Statistical education, publishing, sports analytics, and game theory - everything that makes math useful in real life. Now carbon negative!
Featured post
Textbook: Writing for Statistics and Data Science
If you are looking for my textbook Writing for Statistics and Data Science here it is for free in the Open Educational Resource Commons. Wri...
Monday, 13 November 2017
Wednesday, 8 November 2017
Writing a Resume as a Data Scientist or Statistician.
What do viral posts and resumes have in common? They rise to the top based on a very superficial evaluation.
Here are some notes from a seminar on resume writing for students, especially undergraduates, in statistics and data science.
Here are some notes from a seminar on resume writing for students, especially undergraduates, in statistics and data science.
Thursday, 2 November 2017
Evaluating Exam Questions using Crowdmark, IRT, and the Generalized Partial Credit Model
Making good exam questions
is universally hard. The ideal question should have a clear solution
to those with the requisite understanding, but also difficult enough
that someone without the knowledge needed can guess at an answer.
An item response theory (IRT) based analysis can estimate the difficulty of a question, as well as the general skill of each of the test takers. The generalized partial credit model extends classical IRT from questions with binary scores to ones with an ordinal set of possible scores.
R code and example inside.
An item response theory (IRT) based analysis can estimate the difficulty of a question, as well as the general skill of each of the test takers. The generalized partial credit model extends classical IRT from questions with binary scores to ones with an ordinal set of possible scores.
R code and example inside.
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