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thomvolker.github.io | ||
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www.listendata.com
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| | | | | [AI summary] The user is seeking guidance on performing linear regression analysis in R, including data preparation, model building, and interpretation. They have questions about multicollinearity, variable selection, and package usage. The response should provide step-by-step instructions on installing necessary packages, conducting analysis, and addressing common issues. | |
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www.jeremykun.com
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| | | | | The singular value decomposition (SVD) of a matrix is a fundamental tool in computer science, data analysis, and statistics. It's used for all kinds of applications from regression to prediction, to finding approximate solutions to optimization problems. In this series of two posts we'll motivate, define, compute, and use the singular value decomposition to analyze some data. (Jump to the second post) I want to spend the first post entirely on motivation and background. | |
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statsandr.com
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| | | | | Learn how to run multiple and simple linear regression in R, how to interpret the results and how to verify the conditions of application | |
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www.seascapemodels.org
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| | | [AI summary] A technical blog post explains how to interpret the Akaike Information Criterion (AIC) by deriving the statistics in R, comparing model likelihoods, and selecting the most parsimonious hypothesis. | ||
