Easy Statistics: Regression Modelling

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

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
Requirements
Students should have a basic idea of linear regression
Check my "Easy Statistics: Linear Regression" course if you need a primer
Description
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

To learn and understand the basic approaches to regression modelling

To learn, in an easy manner, tips and tricks to improve your regression models

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

Who this course is for
Students working with data and quants
Anyone who wants to understand regression easily and build better models
Those in the Economics/Politics/Social Sciences
Business managers using quantitative evidence

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