Data Analysis - What is Non-Linear Regression

An easy introduction to Non-Linear 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 basic non-linear regression in data analysis; specifically logit and probit modelling.
This class will explain what regression is and how Logit and Probit regression works. Logit and Probit modelling is often used when analysing choice and other discrete outcomes. Both methods introduce important non-linear concepts that are used by more advanced methods.
The class with not use 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 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 non-linear regression
To be at ease with regression terminology
To be able to comfortably interpret and analyze logit/probit regression output
To learn tips and tricks
Specific topics that will be covered are
What kinds of Non-Linear regression analysis exist
How does Non-Linear regression work?
Why is Non-Linear regression useful?
What is Maximum Likelihood?
The Linear Probability Model
Logit and Probit regression
Latent variables
Marginal effects
Dummy variables in Logit and Probit regression
Goodness-of-fit statistics
Odd-ratios for Logit models
Practical Logit and Probit model building in Stata
The computer software Stata will be used to demonstrate practical examples.
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