Data Analysis - What is Linear Regression?

An easy introduction to Regression in Data Analysis

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

This class is designed to provide you with a compact, and easy to understand, class that focuses on the basic principles of regression in data analysis.

This class will focus on the understanding and applying linear regression in data analysis

This class will explain what regression is and how Ordinary Least Squares (OLS) works. It will do this without any equations or mathematics. The focus of this class is on application and interpretation of regression in data analysis. The learning on this class is underpinned by many animated graphics that demonstrate particular concepts.

No prior knowledge is necessary and this class is for anyone who would like to engage with quantitative analysis.

The main learning outcomes are

To learn and understand the basic intuition behind linear regression
To be at ease with regression terminology
To be able to comfortably interpret and analyze regression output
To learn tips and tricks
Specific topics that will be covered are

What kinds of regression analyses exist
Correlation versus causation
Parametric and non-parametric methods
The least squares method
R-squared
Beta's, standard errors
T-statistics, p-values and confidence intervals
Best Linear Unbiased Estimator
The Gauss-Markov assumptions
Bias versus efficiency
Homoskedasticity
Collinearity
Functional form
Zero conditional mean
Regression in logs
Practical model building
Understanding regression output
Presenting regression output
The computer software Stata will be used to demonstrate practical examples.