/explore
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debrouwere.org | ||
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ddarmon.github.io
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statsandr.com
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| | | | | Learn how to perform a descriptive analysis of your data by hand. You will learn how to compute both location and dispersion measures to describe your data | |
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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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www.rdatagen.net
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| | | We've finally reached the end of the road. This is the fifth and last post in a series building up to a Bayesian proportional hazards model for analyzing a stepped-wedge cluster-randomized trial. If you are just joining in, you may want to start at the beginning. The model presented here integrates non-linear time trends and cluster-specific random effects-elements we've previously explored in isolation. There's nothing fundamentally new in this post; it brings everything together. Given that the groundwork has already been laid, I'll keep the commentary brief and focus on providing the code. | ||
