Statistics in Python: Multiple regression

This video is part of a full-length course on Python programming, including 32+ hours of video instruction and 80+ hours of exercises. The course starts from the absolute basics (no prior knowledge of Python is necessary!) and builds up towards intermediate-level competency.

Course topics include:
- Data types
- Indexing and slicing
- Functions
- Flow control
- Text and data visualization

Here's a list of all the projects you can complete:
- Project 1: Text search and replace
- Project 2: The Law of Large Numbers
- Project 3: Entropy of written English
- Project 4: State-space trajectories
- Project 5: Statistics
- Project 6: Spectral analysis
- Project 7: The colorful rainbow of noise
- Project 8: Awesome mathy stuff
- Project 9: Denoising noisy signals
- Project 10: Time series filtering
- Project 11: Descriptive stats without numpy
- Project 12: Clustering: PCA, t-SNE, and k-means
- Project 13: Index your Python code
- Project 14: Local minimum via gradient descent
- Project 15: Data curve fitting
- Project 16: Time-frequency analysis of EEG
- Project 17: Interpolation and extrapolation
- Project 18: Simulate a brain circuit

[jenschkies]

this video is a treasure! Thank you so much!

[piusnnwachukwu]

Nice video, full of insight. Although that I followed the video with my own datasets, but my graphs didn't show. I think that the problem is that I didn't watch the previous video you referenced and hence, couldn't understand the exact variable you named=data in your variables?

[Mrjacksurvival]

Nice video. How to do it when both variables have uncertainty in the measures?