Explore >> Select a destination
|
You are here 
|
ddarmon.github.io | ||
| | | | |
statsandr.com
|
|
| | | | | Learn how to run multiple and simple linear regression in R, how to interpret the results and how to verify the conditions of application | |
| | | | |
debrouwere.org
|
|
| | | | | [AI summary] The article discusses the limitations of traditional descriptive statistics like the mean, standard deviation, and correlation, advocating for more intuitive and robust measures. It emphasizes the importance of understanding data through alternative metrics such as medians, interquartile ranges, and percentile ranks, which are better suited for interpretation and communication. The piece also addresses the challenges of working with skewed data, outliers, and high-dimensional datasets, suggesting practical approaches like histograms and robust statistical methods. The author highlights the need for descriptive statistics to be more user-friendly and accessible, rather than being primarily focused on inferential analysis. | |
| | | | |
cyclostationary.blog
|
|
| | | | | Our toolkit expands to include basic probability theory. | |
| | | | |
zevross.com
|
|
| | | We use the R library mgcv for modeling environmental data with generalized additive models (GAMs). It's a great library loaded with functionality but we often find that the default diagnostic ... | ||
