Easy Statistics: Non-Linear Regression

An easy introduction to Logit and Probit regression.

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 Non-Linear regression, specifically Logit and Probit regression.

This course will explain what non-linear regression is and how Logit and Probit regression works. It will do this without equations or mathematics. The focus of this course is on application and interpretation of regression. The learning on this course is underpinned by animated graphics that demonstrate particular statistical concepts.

No prior knowledge is necessary and this course is for anyone who needs to engage with quantitative analysis.

The main learning outcomes are:

1. To learn and understand the basic statistical intuition behind Non-Linear regression

2. To learn and understand how Logit and Probit models work

3. To be able to comfortably interpret and analyze complicated regression output from Logit and Probit regression

4. To learn tips and tricks around Non-Linear Regression analysis

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.

Who this course is for:

• Academic students of any level.
• Practitioners who require quantitative knowledge.
• Business users and managers who engage with quantitative reports.
• Government workers who are involved in policy analysis.
• Anyone who has an interest in, or needs to engage, with statistical regression.
• Those needing to analyse choice models and other categorical dependent variables regression models.