Easy Statistics: Regression Modelling

Learn tips and trick how to build better regression models. Part of the Easy Statistics series.

Learning and applying new statistical techniques can often be a daunting experience.

What you’ll learn

  • Tips for Building Regression Models.
  • The Philosophy Behind Regression.
  • Polynomial Regression.
  • Interaction Effects in Regression.
  • Using Time in Regression.
  • How to use Categorical Explanatory Variables.
  • Dealing with Multicollinearity.
  • How to Handle Missing Data.

Course Content

  • Introduction –> 1 lecture • 6min.
  • Regression Modelling – Don’t Rush It –> 1 lecture • 15min.
  • Non-Linear Functional Form in Regression –> 2 lectures • 22min.
  • Interaction Effects in Regression –> 2 lectures • 21min.
  • Using Time in Regression –> 2 lectures • 24min.
  • Categorical Explanatory Variables in Regression –> 2 lectures • 20min.
  • Dealing with Multicollinearity in Regression –> 2 lectures • 27min.
  • Dealing with Missing Data in Regression –> 2 lectures • 31min.
  • Proportional and fractional data –> 2 lectures • 13min.

Easy Statistics: Regression Modelling


  • Students should have a basic idea of linear regression.
  • Check my “Easy Statistics: Linear Regression” course if you need a primer.

Learning and applying new statistical techniques can often be a daunting experience.

“Easy Statistics” is designed to provide you with a compact, and easy to understand, course that focuses on the basic principles of statistical methodology.

This course will focus on the concept of regression modelling.

Understanding how regression analysis works is only half the battle.

There are many pitfalls to avoid and tricks to learn when modelling data in a regression setting. Often, it takes years of experience to accumulate these. In these videos, I will outline some of the most common modelling issues. What is the theory behind them, what do they do and how can we deal with them?

Each topic has a practical demonstration in Stata and includes relevant Stata code. However, Stata is not required to follow this course.

The main learning outcomes are:

  1. To learn and understand the basic approaches to regression modelling
  2. To learn, in an easy manner, tips and tricks to improve your regression models
  3. To gain practical experience

Themes include:

  • Fundamental of Regression Modelling – What is the Philosophy?
  • Functional Form – How to Model Non-Linear Relationships in a Linear Regression
  • Interaction Effects – How to Use and Interpret Interaction Effects
  • Using Time – Exploring Dynamics Relationships with Time Information
  • Categorical Explanatory Variables – How to Code, Use and Interpret them
  • Dealing with Multicollinearity – Excluding and Transforming Collinear Variables
  • Dealing with Missing Data – How to See the Unseen
  • Fractional regression modelling – How to model proportional data


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