Explore >> Select a destination
|
You are here 
|
statsandr.com | ||
| | | | |
indrajeetpatil.github.io
|
|
| | | | | Extension of ggplot2, ggstatsplot creates graphics with details from statistical tests included in the plots themselves. It provides an easier syntax to generate information-rich plots for statistical analysis of continuous (violin plots, scatterplots, histograms, dot plots, dot-and-whisker plots) or categorical (pie and bar charts) data. Currently, it supports the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, meta-analysis, and regression analyses. References: Patil (2021) . | |
| | | | |
blog.minitab.com
|
|
| | | | | Regression Analysis: How Do I Interpret R-squared and Assess the Goodness-of-Fit? | |
| | | | |
entropicthoughts.com
|
|
| | | | | [AI summary] The text explores various statistical concepts, including correlation, variance, and partial correlations, using examples like income and room count, word count predictions by AI models, and programming language commit sizes. It discusses methods for calculating and interpreting these correlations, as well as the importance of considering degrees of freedom in statistical analysis. The article also references historical statistical methods and emphasizes the value of multiple perspectives in understanding statistical relationships. | |
| | | | |
sebastianraschka.com
|
|
| | | I'm Sebastian: a machine learning & AI researcher, programmer, and author. As Staff Research Engineer Lightning AI, I focus on the intersection of AI research, software development, and large language models (LLMs). | ||
