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Figure 6 Quantitative results over the HumanEva data set where score MMTA is displayed in pseudocolor.
where Score(c j,r i ) quantifies the perceived importance that a client c j associates with a requirement r i assigning a value ranging from 0 (no importance) to 10 (the highest importance).
The occurrence time is shown at the top of each focal solution, and the magnitude and score are shown at the bottom where "score" means a ratio of the data consistent to the obtained solution to the total data.
where score i t is the PISA reading score for student i in year t (t =2000, 2009), X is a vector of individual characteristics, and u i t is an error term with mean zero and variance σt 2. In order to choose which variables to include in the vector X of individual characteristics, we follow the international literature (Hanushek & Woessmann, 2011).
Where Score ( I ) is score of user I, Messages are messages of user I, Responses are concepts in the response of the message M, Rep (R) is the number of iterations of concept R in the response of the message M and Weight (R) is weight of concept R in the response of the message M. Based on equation (3), it is obvious that score of each user has been calculated based on two measures.
where score is t is the PISA reading score for student i in year t (t =2000, 2009) and school s, X is a vector for individual characteristics, S is a vector of school characteristics, and u is t is an error term with mean zero and variance σt 2. Among the school characteristics, we include the proportion of foreign language speakers in the school.
Similar(29)
The most notable improvement was in middle schools, where scores have traditionally lagged.
But Mr. Unz did not focus on districts where scores had increased while bilingual classes continued.
Unlike the math results, where scores along each level of achievement rose, reading scores have remained fairly stagnant since 1992.
There are a few situations in football where scoring an easy touchdown is the wrong thing to do.
It is hard to think of another occasion in a football match where scoring a goal is so likely.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com