FavoriteLoadingBookmark this content

DataCamp – Statistical Modeling in R (Part 2) No ratings yet.

Link to Content:
DataCamp Statistical Modeling in R Part 2

Created/Published/Taught by:
Daniel Kaplan
Nick Carchedi
Tom Jeon

Content Found Via:

Free? Partially: Some Free Content, Some Paid

Cost Range:
$0.00 - $29.00

Tags: /
Content Type: /

Difficulty Rating:

No ratings yet.

Part 1 of this course can be found here: Statistical Modeling in R Part 1.

From DataCamp:

“Statistical Modeling in R is a multi-part course designed to get you up to speed with the most important and powerful methodologies in statistics. In Part 2, we’ll take a look at effect size and interaction, the concepts of total and partial change, sampling variability and mathematical transforms, and the implications of something called collinearity. This course has been written from scratch, specifically for DataCamp users. As you’ll see, by using computing and concepts from machine learning, we’ll be able to leapfrog many of the marginal and esoteric topics encountered in traditional ‘regression’ courses.”

This course consists of four chapters:

  1. Effect size and interaction
  2. Total and partial change
  3. Sampling variability and mathematical transforms
  4. Variables working together

Recommended Prerequisites: Statistical Modeling in R Part 1

Go to Content: DataCamp – Statistical Modeling in R (Part 2)