1.010 Uncertainty in Engineering
Introduces probability and statistics with an emphasis on understanding, quantifying, and modeling uncertainty. Topics include events and their probability, the total probability and Bayes' theorems, discrete and continuous random variables and vectors, covariance, correlations, and conditional analysis. Random sampling, estimation of distribution parameters (method of moments, maximum likelihood, Bayesian estimation), and simple and multiple linear regression. Concepts illustrated with examples from various areas of engineering and everyday life. Integrates applications with statistical computing and graphics.
This class has 18.02 as a prerequisite.
1.010 will be offered this semester (Fall 2017). It is instructed by S. Saavedra.
Lecture occurs 9:00 AM to 10:30 AM on Tuesdays and Thursdays in 1-242.
This class counts for a total of 12 credits.
You can find more information at the http://www.google.com/search?&q=MIT+%2B+1.010&btnG=Google+Search&inurl=https site or on the 1.010 Stellar site.
© Copyright 2015 Yasyf Mohamedali