Easy Statistics: Regression Modelling Tips
Learn tips and trick how to build better regression models.
Regression Analysis,Statistics,Business,Business Intelligence
Lectures -12
Duration -2 hours
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Course 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
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
1 Lectures
- Introduction 06:05 06:05
Regression Modelling - Don't Rush It
1 Lectures
Non-Linear Functional Form in Regression
2 Lectures
Interaction Effects in Regression
2 Lectures
Time in Regression
2 Lectures
Categorical Explanatory Variables in Regression
2 Lectures
Dealing with Multicollinearity in Regression
2 Lectures
Instructor Details
F Buscha
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