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A rendition of the Fry graph. The Fry readability formula (or Fry readability graph) is a readability metric for English texts, developed by Edward Fry. [1] The grade reading level (or reading difficulty level) is calculated by the average number of sentences (y-axis) and syllables (x-axis) per hundred words. These averages are plotted onto a ...
The automated readability index (ARI) is a readability test for English texts, designed to gauge the understandability of a text. Like the Flesch–Kincaid grade level, Gunning fog index, SMOG index, Fry readability formula, and Coleman–Liau index, it produces an approximate representation of the US grade level needed to comprehend the text.
Readability is the ease with which a reader can understand a written text.The concept exists in both natural language and programming languages though in different forms. In natural language, the readability of text depends on its content (the complexity of its vocabulary and syntax) and its presentation (such as typographic aspects that affect legibility, like font size, line height ...
The Raygor estimate graph is a readability metric for English text. It was developed by Alton L. Raygor, who published it in 1977. [1] The US grade level is calculated by the average number of sentences and letters per hundred words. These averages are plotted onto a specific graph where the intersection of the average number of sentences and ...
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Linsear Write is a readability metric for English text, purportedly developed for the United States Air Force to help them calculate the readability of their technical manuals. [1] It is one of many such readability metrics, but is specifically designed to calculate the United States grade level of a text sample based on sentence length and the ...
"The Flesch–Kincaid" (F–K) reading grade level was developed under contract to the U.S. Navy in 1975 by J. Peter Kincaid and his team. [1] Related U.S. Navy research directed by Kincaid delved into high-tech education (for example, the electronic authoring and delivery of technical information), [2] usefulness of the Flesch–Kincaid readability formula, [3] computer aids for editing tests ...
According to their research, 29% of 791 large-cap equity funds beat the S&P 500 in 2019. Of those funds, 75% beat the benchmark again in 2020. But only 9.1%, or 21 funds, were able to extend that ...