normal curve table - перевод на русский
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normal curve table - перевод на русский

STATISTICAL MEASURE RELATED TO PERCENTILE RANK
Normal Curve Equivalent

normal curve table      
таблица нормального распределения
normal curve table      
таблица нормального распределения
bell curve         
  • [[Carl Friedrich Gauss]] discovered the normal distribution in 1809 as a way to rationalize the [[method of least squares]].
  • As the number of discrete events increases, the function begins to resemble a normal distribution
  • Comparison of probability density functions, <math>p(k)</math> for the sum of <math>n</math> fair 6-sided dice to show their convergence to a normal distribution with increasing <math>na</math>, in accordance to the central limit theorem. In the bottom-right graph, smoothed profiles of the previous graphs are rescaled, superimposed and compared with a normal distribution (black curve).
  • Histogram of sepal widths for ''Iris versicolor'' from Fisher's [[Iris flower data set]], with superimposed best-fitting normal distribution.
  • Fitted cumulative normal distribution to October rainfalls, see [[distribution fitting]]
  •  [[Pierre-Simon Laplace]] proved the [[central limit theorem]] in 1810, consolidating the importance of the normal distribution in statistics.
  • The [[bean machine]], a device invented by [[Francis Galton]], can be called the first generator of normal random variables. This machine consists of a vertical board with interleaved rows of pins. Small balls are dropped from the top and then bounce randomly left or right as they hit the pins. The balls are collected into bins at the bottom and settle down into a pattern resembling the Gaussian curve.
  • '''a:''' Probability density of a function <math>\cos x^2</math> of a normal variable <math>x</math> with <math>\mu=-2</math> and <math>\sigma=3</math>. '''b:''' Probability density of a function <math>x^y</math> of two normal variables <math>x</math> and <math>y</math>, where <math>\mu_x=1</math>, <math>\mu_y=2</math>, <math>\sigma_x = 0.1</math>, <math>\sigma_y = 0.2</math>, and <math>\rho_{xy} = 0.8</math>. '''c:''' Heat map of the joint probability density of two functions of two correlated normal variables <math>x</math> and <math>y</math>, where <math>\mu_x = -2</math>, <math>\mu_y=5</math>, <math>\sigma_x^2 = 10</math>, <math>\sigma_y^2 = 20</math>, and <math>\rho_{xy} = 0.495</math>. '''d:''' Probability density of a function <math display="inline">\sum_{i=1}^4 \vert x_i \vert</math> of 4 iid standard normal variables. These are computed by the numerical method of ray-tracing.<ref name="Das" />
  • The ground state of a [[quantum harmonic oscillator]] has the [[Gaussian distribution]].
  • For the normal distribution, the values less than one standard deviation away from the mean account for 68.27% of the set; while two standard deviations from the mean account for 95.45%; and three standard deviations account for 99.73%.
PROBABILITY DISTRIBUTION
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['belkə:v]

общая лексика

кривая нормального распределения

математика

колоколообразная

гауссова кривая

Определение

bell-shaped

Википедия

Normal curve equivalent

In educational statistics, a normal curve equivalent (NCE), developed for the United States Department of Education by the RMC Research Corporation, is a way of normalizing scores received on a test into a 0-100 scale similar to a percentile rank, but preserving the valuable equal-interval properties of a z-score.

It is defined as:

70770 + /qnorm(.99) × z

or, approximately

50 + 21.063 × z,

where z is the standard score or "z-score", i.e. z is how many standard deviations above the mean the raw score is (z is negative if the raw score is below the mean). The reason for the choice of the number 21.06 is to bring about the following result: If the scores are normally distributed (i.e. they follow the "bell-shaped curve") then

  • the normal equivalent score is 99 if the percentile rank of the raw score is 99;
  • the normal equivalent score is 50 if the percentile rank of the raw score is 50;
  • the normal equivalent score is 1 if the percentile rank of the raw score is 1.

This relationship between normal equivalent scores and percentile ranks does not hold at values other than 1, 50, and 99. It also fails to hold in general if scores are not normally distributed.

The number 21.06 was chosen because

  • It is desired that a score of 99 correspond to the 99th percentile;
  • The 99th percentile in a normal distribution is 2.3263 standard deviations above the mean;
  • 99 is 49 more than 50—thus 49 points above the mean;
  • 49/2.3263 = 21.06.

Normal curve equivalents are on an equal-interval scale. This is advantageous compared to percentile rank scales, which suffer from the problem that the difference between any two scores is not the same as that between any other two scores (see below or percentile rank for more information).

The major advantage of NCEs over percentile ranks is that NCEs can be legitimately averaged.

Как переводится normal curve table на Русский язык