/explore
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thomvolker.github.io | ||
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www.aleksandrhovhannisyan.com
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| | | | | Some systems of equations do not have a unique solution, but we can find an approximate solution using the method of least squares. Applications of this method include linear and polynomial regression. | |
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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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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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svmiller.com
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| | | Linear model diagnostics for linearity and heteroskedasticity can induce you to make different design choices, which have implications for statistical significance. The example here is the effect o... | ||
