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Understanding Regression Techniques
Rating: 4.5 out of 5(152 ratings)
3,655 students

Understanding Regression Techniques

An Introduction to Predictive Analytics for Data Scientists
Created byNajib Mozahem
Last updated 6/2019
English

What you'll learn

  • Understand what regression is
  • Build linear regression models
  • Build logistic regression models
  • Build count models
  • Interpret regression results
  • Visualise the results
  • Test model assumptions

Course content

15 sections89 lectures7h 10m total length
  • Introduction3:15
  • Simple linear regression4:26
  • The slope5:29
  • R-squared5:30
  • The p-value7:06
  • Model fit2:34
  • The residuals5:16

Requirements

  • none

Description

Included in this course is an e-book and a set of slides. The purpose of the course is to introduce the students to regression techniques. The course covers linear regression, logistic regression and count model regression. The theory behind each of these three techniques is described in an intuitive and non-mathematical way. Students will learn when to use each of these three techniques, how to test the assumptions, how to build models, how to assess the goodness-of-fit of the models, and how to interpret the results. The course does not assume the use of any specific statistical software. Therefore, this course should be of use to anyone intending on applying regression techniques no matter which software they use. The course also walks students through three detailed case studies.

Who this course is for:

  • Beginner data science students
  • Business statistics students