This practical introduction to statistics written with chemical engineers in mind, emphasizes real-world problem solving. Including over 100 Matlab and Python examples, accompanied by lecture slides, code and solutions for instructors, it is ideal for chemical engineering students keen to advance to courses in data science and machine learning.
This practical introduction to statistics written with chemical engineers in mind, emphasizes real-world problem solving. Including over 100 Matlab and Python examples, accompanied by lecture slides, code and solutions for instructors, it is ideal for chemical engineering students keen to advance to courses in data science and machine learning.
Victor M. Zavala is the Baldovin-DaPra Professor of Chemical and Biological Engineering at the University of Wisconsin, Madison and a Senior Computational Mathematician at Argonne National Laboratory. He is the recipient of the Harvey Spangler Award for Innovative Teaching and Learning Practices from the College of Engineering at UW-Madison, and of the Presidential Early Career Award for Scientists and Engineers (PECASE).
Inhaltsangabe
1. Introduction to statistics 2. Univariate random variables 3. Multivariate random variables 4. Estimation for random variables 5. Estimation for structural models 6. Statistical learning 7. Decision-making under uncertainty.
1. Introduction to statistics 2. Univariate random variables 3. Multivariate random variables 4. Estimation for random variables 5. Estimation for structural models 6. Statistical learning 7. Decision-making under uncertainty.
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