Connect the Dots: Linear and Logistic Regression [Video]

More Information
  • Build robust linear models that stand up to scrutiny in Excel, R and Python
  • Use simple and multiple regression to explain variance
  • Use simple and multiple regression to predict an outcome
  • Interpret the results of a regression
  • Understand the risks involved in regression and avoid common pitfalls

This course will teach you how to build robust linear models and do logistic regression in Excel, R and Python. Let’s parse that. Robust linear models: Linear Regression is a powerful method for quantifying the cause and effect relationships that affect different phenomena in the world around us. This course will teach you how to build robust linear models that will stand up to scrutiny when you apply them to real world situations. Logistic regression: Logistic regression has many cool applications: analyzing consequences of past events, allocating resources, solving binary classification problems using machine learning and so on. This course will help you understand the intuition behind logistic regression and how to solve it using cookie-cutter techniques. Excel, R and Python: Put what you've learnt into practice. Leverage these powerful analytical tools to build models for stock returns.

Style and Approach

A 5-hour course with good, understandable, explanation with examples as usual for the Loony style. You won't be calculating the stuff by hand after, it is not that detailed, but it gives a useful understanding to be able to apply the tools

  • Method of least squares, Explaining variance, Forecasting an outcome
  • Residuals, assumptions about residuals
  • Implement simple regression in Excel, R and Python
  • Interpret regression results and avoid common pitfalls
  • Implement Multiple regression in Excel, R and Python
  • Introduce a categorical variable
  • Applications of Logistic Regression, the link to Linear Regression and Machine Learning
  • Solving logistic regression using Maximum Likelihood Estimation and Linear Regression
  • Extending Binomial Logistic Regression to Multinomial Logistic Regression
  • Implement Logistic regression to build a model stock price movements in Excel, R and Python


Course Length 4 hours 45 minutes
ISBN 9781788991957
Date Of Publication 16 Dec 2017



Loonycorn is Janani Ravi and Vitthal Srinivasan. Between them, they have studied at Stanford, been admitted to IIM Ahmedabad, and have spent years working in tech, in the Bay Area, New York, Singapore and Bangalore. Janani spent 7 years at Google (New York, Singapore); Studied at Stanford and also worked at Flipkart and Microsoft. Vitthal also worked at Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too. They think they might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why they are so excited to be here. They hope you will try their offerings, and you'll like them.