(This is a portion of a live class via Zoom on February 16, 2021, for my Engineering Computations course at the George Washington University.)
Corresponding written lesson at: http://go.gwu.edu/engcomp1lesson5
This is a hands-on lesson of linear regression using Jupyter Notebooks in Python. Starting by reusing notes from a previous class, we clean and update a notebook, refining it for our current lesson on regression analysis. We guide you through executing cells, editing markdown, and leveraging useful utilities like MathPix for equation rendering. Learn how to compute mean values using both custom functions and NumPy's built-in functions, and how to calculate and visualize the best-fit line. By the end of this session, you'll be able to write a function to compute regression coefficients and plot linear regression models efficiently.
00:00 Introduction and Setup
00:23 Copying and Renaming Notebooks
00:58 Executing and Editing Cells
01:17 Linear Regression Basics
04:23 Using MathPix for Equations
06:10 Computing Mean Values
07:43 Exploring NumPy Functions
10:29 Calculating Regression Coefficients
18:16 Plotting the Regression Line
20:12 Enhancing the Plot
