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The field of machine learning is all about feeding huge amounts of data into algorithms to make accurate predictions. Statistics is concerned with predictions as well, says Tibshirani, but also with determining how confident we can be about the importance of certain inputs.
This is because the authors focus on intuition rather than mathematics.
He believes this helps them think conceptually. And also, a situation where it might not work.
I think people really appreciate that. Bootstrapping is a way to assess the accuracy of an estimate by generating multiple datasets from the same data. For example, lets say you collected the weights of 1, randomly selected adult women in the US, and found that the average was pounds.
How confident can you be in this number? In conventional statistics, to answer this question you would use a formula developed more than a century ago, which relies on many assumptions.
He received his B. His first academic job was as an assistant professor in the Department of Statistics at Purdue University.
While there, he taught the introductory concepts course with Professor Moore and as a result of this experience he developed an interest in statistical education. He is the author of several research papers and of a book on the design and analysis of computer experiments.
He is an elected fellow of the American Statistical Association. He has served as the editor of the journal Technometrics and as editor of the Journal of Statistics Education. Michael A.
Fligner Michael A. He has done consulting work with several large corporations in Central Ohio.
Professor Fligner's research interests are in Nonparametric Statistical methods and he received the Statistics in Chemistry award from the American Statistical Association for work on detecting biologically active compounds.