z scores - translation to ισπανικά
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z scores - translation to ισπανικά

HOW MANY STANDARD DEVIATIONS APART FROM THE MEAN AN OBSERVED DATUM IS
Standardized (statistics); Z-score; Z score; Z scores; Standardized score; Standardized variable; Standardised scores; Standardizing; Standardize; Z statistic; Z-Score; T-score; T score; Zscore; Z‐score; Sigma score; Z-scores; Standardization (statistics); Statistical standardization
  • T-scores]]
  • The ''z'' score for Student B was 0.6, meaning Student B was 0.6 standard deviation above the mean. Thus, Student B performed in the 72.57 percentile on the SAT.
  • The ''z'' score for Student A was 1, meaning Student A was 1 standard deviation above the mean. Thus, Student A performed in the 84.13 percentile on the SAT.

z scores         
puntuación de sobrevivencia (índices que sirven para medir la capacidad de quedar en bancarrota (en contabilidad))
standardize         
estandarizar
ceda         
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  • Dibujo de un gato durmiendo
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ÚLTIMA LETRA DEL ALFABETO LATINO
Zeta (letra); ⠵; Z (letra); Zeda; Ceda
n. truce; give, ability to yield

Ορισμός

zeda
zeda f. Variante ortográfica de "ceda", nombre de la letra "z".

Βικιπαίδεια

Standard score

In statistics, the standard score is the number of standard deviations by which the value of a raw score (i.e., an observed value or data point) is above or below the mean value of what is being observed or measured. Raw scores above the mean have positive standard scores, while those below the mean have negative standard scores.

It is calculated by subtracting the population mean from an individual raw score and then dividing the difference by the population standard deviation. This process of converting a raw score into a standard score is called standardizing or normalizing (however, "normalizing" can refer to many types of ratios; see normalization for more).

Standard scores are most commonly called z-scores; the two terms may be used interchangeably, as they are in this article. Other equivalent terms in use include z-values, normal scores, standardized variables and pull in high energy physics.

Computing a z-score requires knowledge of the mean and standard deviation of the complete population to which a data point belongs; if one only has a sample of observations from the population, then the analogous computation using the sample mean and sample standard deviation yields the t-statistic.