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aurimas.eu | ||
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ewulczyn.github.io
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| | | | | Data Scientist @ WMF | |
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www.randomservices.org
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| | | | | [AI summary] The text covers various topics in probability and statistics, including continuous distributions, empirical density functions, and data analysis. It discusses the uniform distribution, rejection sampling, and the construction of continuous distributions without probability density functions. The text also includes data analysis exercises involving empirical density functions for body weight, body length, and gender-specific body weight. | |
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tachy.org
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| | | | | Notes on p-values. | |
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www.rdatagen.net
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| | | In my previous post, I described a continuous data generating process that can be used to generate discrete, categorical outcomes. In that post, I focused largely on binary outcomes and simple logistic regression just because things are always easier to follow when there are fewer moving parts. Here, I am going to focus on a situation where we have multiple outcomes, but with a slight twist - these groups of interest can be interpreted in an ordered way. | ||
