Reason #3: RStudio’s shiny
Understanding Model Assumptions
Types of Error
Illustrating Fine Points
Introduction My last blog article shows how to build an interactive recommendation engine in Tableau using a simple model utilizing the cosine similarity measure. While this can be a good way to explore unknown data, it is wise to validate any model before...
As I have worked on various projects at Etsy, I have accumulated a suite of functions that help me quickly produce tables and charts that I find useful. Because of the nature of iterative development, it often happens that I … Continue reading →
Now that we have reached the milestone of 30,000 (!) enthusiastic R students, and the DataCamp platform is paving its way into academics and professional organizations, we felt it was time to take our design to a higher level. So as of this week, your favourite free learning platform for R tutorials and data science will have a totally new look and feel!
I created a small program called Singleton Remover CSV to help researchers process files quickly that might otherwise take a long time to be handled by hand. The program simply compares ID values given in the first two columns of a .csv file, saves any...
DataCamp first to offer free interactive R tutorials via the OpenIntro platform. In one week, the ten-week Coursera course on Data Analysis and Statistical Inference by prof. Mine Çetinkaya-Rundel of Duke University comes to an end. At DataCamp it was one of our first experiences providing interactive R exercises on a large scale, and we’re proud to say
(Generalized) Linear models make some strong assumptions concerning the data structure: Independance of each data points Correct distribution of the residuals Correct specification of the variance structure Linear relationship between the response and the linear predictor For simple lm 2-4) means that the residuals should be normally distributed, the variance should be homogenous across the