Linear regression is a key technique used in forecasting and in quantifying cause-effect relationships. In this course, Understanding and Applying Linear Regression, you will learn how to identify patterns in data and test those relationships for statistical soundness. You will also learn simple regression and multiple regression. Finally, you’ll explore the use of categorical variables. When you’re finished with this course, you will have a strong applied knowledge of regression in Excel, R, and Python that will help with factor analysis, logistic regression, and other powerful techniques.
Author Name: Vitthal Srinivasan
Author Description:
Vitthal has spent a lot of his life studying – he holds Masters Degrees in Math and Electrical Engineering from Stanford, an MBA from INSEAD, and a Bachelors Degree in Computer Engineering from Mumbai. He has also spent a lot of his life working – as a derivatives quant at Credit Suisse in New York, then as a quant trader, first with a hedge fund in Greenwich and then on his own, and finally at Google in Singapore and Flipkart in Bangalore. In all these roles, he has written a lot of code, and b… more

Table of Contents

• Course Overview
  2mins
• Modeling Relationships Between Variables Using Regression
  32mins
• Understanding Simple Regression Models
  36mins
• Implementing Simple Regression Models in Excel
  30mins
• Implementing Simple Regression Models in R
  24mins
• Implementing Simple Regression Models in Python
  16mins
• Understanding Multiple Regression Models
  40mins
• Implementing Multiple Regression Models in Excel
  39mins
• Implementing Multiple Regression Models in R
  15mins
• Implementing Multiple Regression Models in Python
  15mins