9. Understanding Experimental Data

MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
Instructor: Eric Grimson

Prof. Grimson talks about how to model experimental data in a way that gives a sense of the underlying mechanism and to predict behavior in new settings.

License: Creative Commons BY-NC-SA

[mikets]

""regression" does not relate to error minimization. The term "regression" appeared first in the article describing statistics of people's height through generations. If a father was tall, his son would be likely taller than average, but ... less so because it is a "regression to the mean". See The Art of Statistics: Learning from Data by David Spiegelhalter for more details.

[shobhamourya]

@44:44 Best ever explanation of coefficient of determination R and variability R^2

[leixun]

*My takeaways:*
1. An example: spring model 3:43
2. Coefficient of determination 38:03

[nealyee]

These jokes are so cool that I would hang out with them for sure

[haneulkim]

Fun, on point, and in-depth lecture. Thanks you MIT.

[rgi]

Trivia: while dealing with real data, one might not want R2 to get close to 1, as that might indicate overfitting, which is really not good, especially for prediction models, which is nicely illustrated by the case of 16-degree polynomial

[ParisienDBS]

Out of curiosity, at 19:01, what would trying to minimize the area of the triangle result in? as opposed to minimizing the distance y?

[cjlion]

would have been nice to see the slides