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ddarmon.github.io | ||
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brenocon.com
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| | | | | [AI summary] The provided text is a collection of comments and discussions from a blog post that originally criticized artificial neural networks (ANNs) in 2008. The comments reflect a range of opinions and debates about the relationship between machine learning (ML) and statistics, with some users defending ML techniques like support vector machines (SVMs), probabilistic graphical models, and deep learning, while others argue for the importance of statistical methods. There are also discussions about the need for better communication between disciplines, the limitations of ML approaches, and the importance of understanding the underlying assumptions of models. The text includes recommendations for textbooks and courses, such as 'All of Statistics' and Andre... | |
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errorstatistics.com
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| | | | | Some have asked me why I haven't blogged on the recent follow-up to the ASA Statement on P-Values and Statistical Significance (Wasserstein and Lazar 2016)-hereafter, ASA I. They're referring to the editorial by Wasserstein, R., Schirm, A. and Lazar, N. (2019)-hereafter, ASA II(note)-opening a special on-line issue of over 40 contributions responding to the call... | |
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statsandr.com
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| | | | | Learn how to apply the Student's t-test by hand and in R in order to compare two independent or paired samples with known or unknown variances | |
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finnstats.com
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| | | Nonlinear Regression Analysis in R. We learned about R logistic regression and its applications, as well as MLE line estimation and NLRM. | ||
