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zevross.com | ||
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www.listendata.com
[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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finnstats.com
Nonlinear Regression Analysis in R. We learned about R logistic regression and its applications, as well as MLE line estimation and NLRM. |
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www.fromthebottomoftheheap.net
[AI summary] The text discusses the use of generalized additive models (GAMs) to represent random effects as smooths, enabling the testing of random effects against a null of zero variance. It compares this approach with traditional mixed-effects models (e.g., lmer) and highlights the advantages and limitations of each. Key points include: (1) Representing random effects as smooths in GAMs allows for efficient testing of variance components and compatibility with complex distributional models. (2) While GAMs can fit such models, they are computationally slower for large datasets with many random effects due to the lack of sparse matrix optimization. (3) The AIC values for models with and without random effects are similar, suggesting that the simpler model i... |
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svmiller.com
I believe that the ability to read a regression table is an important task for undergraduate students in political science. I offer an explanation of the regression table in this post. |
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