Easy Statistics: Regression Modelling Tips
Learn tips and trick how to build better regression models.
Updated on Sep, 2023
Language - English
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
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
Check out the detailed breakdown of what’s inside the course
- Introduction 06:05 06:05
Regression Modelling - Don't Rush It
Non-Linear Functional Form in Regression
Interaction Effects in Regression
Time in Regression
Categorical Explanatory Variables in Regression
Dealing with Multicollinearity in Regression
Franz is a Professor of Economics at the University of Westminster. Franz joined the University of Westminster in 2006 after completing his PhD in Economics at Lancaster University.
Franz's personal research interests are in education economics, labor economics, and applied econometrics. Franz has made scientific contributions to issues such as social mobility, measuring the returns to education, the effect of weather of happiness and identity formation. He has been involved in numerous funded research projects from research councils and government departments.
Franz has contributed to wide range of projects including policy evaluation and bespoke econometric advice to UK government departments. These include the Ministry of Defence, HM Revenue and Customs, the Department for Education and the Department for Business, Innovation and Skills.
He has published in leading journals such as Economics of Education Review, the Oxford Bulletin of Economics and Statistics, the British Journal of Political Science and the British Journal of Sociology. Franz has also contributed to numerous policy reports and his research has been covered by media outlets such as BBC news, BBC Radio 4, The Economist, The Guardian, The Times, and Huffington Post. Franz also has a monthly radio program called Policy Matters on Share Radio.
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