normal distribution

İngilizce - Türkçe

normal distribution teriminin İngilizce Türkçe sözlükte anlamı: olağan dağılım; normal frekans dağılımı; normal dağılım; (Dilbilim) düzgün dağılım
log-normal distribution: lognormal dağılım
cumulative normal distribution: (Havacılık) kümülatif normal dağılım
multivariate normal distribution: cok-degiskenli Gauss dagilimi
standard normal distribution: standart ola¤an da¤ilim

İngilizce - İngilizce

normal distribution teriminin İngilizce İngilizce sözlükte anlamı: A family of continuous probability distributions such that the probability density function is the Gaussian function
\varphi_{\mu,\sigma^2}(x) = \frac{1}{\sigma\sqrt{2\pi}} \,e^{ -\frac{(x- \mu)^2}{2\sigma^2}} = \frac{1}{\sigma} \varphi\left(\frac{x - \mu}{\sigma}\right),\quad x\in\mathbb{R}.; a continuous frequency distribution whose graphic representation is a bell-shaped curve that is symmetrical about the mean, which by definition has a mean of 0 and a variance of 1 Many other types of distributions exist that have different shaped curves, such as hypergeometric, Poisson, and binomial; Normal distributions are a family of distributions that are characterised by a bell-shaped, symmetric curve, with scores more concentrated in the middle than in the tails They are defined by two parameters: the mean (m, mu) and the standard deviation (s, sigma) Many kinds of data are approximated well by the normal distribution Many statistical tests assume a normal distribution Most of these tests work well even if the distribution is only approximately normal and in many cases as long as it does not deviate greatly from normality The normal distribution plays a vital role in inference; An important and widely-used distribution in the field of statistics and probability All Normal distributions are symmetric, and the mean and standard deviation values are used as its two distribution parameters; The symmetrical clustering of values around a central location The properties of a normal distribution include the following: (1) It is a continuous, symmetrical distribution; both tails extend to infinity; (2) the arithmetic mean, mode, and median are identical; and, (3) its shape is completely determined by the mean and standard deviation; A term synonymous with the standard normal distribution The normal distribution (a bell-shaped curve) represents a theoretical frequency distribution of measurements In a normal distribution, scores are concentrated near the mean and decrease in frequency as the distance from the mean increases; A specific distribution having a characteristic bell-shaped form; The well known bell shaped curve According to the Central Limit Theorem, the probability density function of a large number of independent, identically distributed random numbers will approach the normal distribution In the fractal family of distributions, the normal distribution only exists when alpha equals 2, or the Hurst exponent equals 0 50 Thus, the normal distribution is a special case which in time series analysis is quite rare See: Alpha, Central Limit Theorem, Fractal Distribution; A common probability distribution displayed by population data If the values of the distribution are plotted on a graph's horizontal axis and their frequency on the vertical axis the pattern displayed is symmetric and bell-shaped The central value or mean represents the peak or the most frequently occurring value; A continuous probability distribution which is used to characterize a wide variety of types of data It is a symmetric distribution, shaped like a bell, and is completely determined by its mean and standard deviation The normal distribution is particularly important in statistics because of the tendency for sample means to follow the normal distribution (this is a result of the Central Limit Theorem) Most classical statistics procedures such as confidence intervals rely on results from the normal distribution The normal is also known as the Gaussian distribution after its originator, Frederich Gauss Parameters: mean mu, standard deviation sigma>0 Domain: all real X Mean: mu Variance: sigma^2; The normal or Gaussian distribution is one of the most important probability density functions, not the least because many measurement variables have distributions that at least approximate to a normal distribution It is usually described as bell shaped, although its exact characteristics are determined by the mean and standard deveiation It arises when the value of a variable is determined by a large number of independent prcoesses For example, weigth is a function of many processes both genetic and environmental Many statistical tests make the assumption that the data come from a normal distribution; A probability distribution in statistics, graphically displayed as a bell-shaped curve; A random variable X has a normal distribution with mean m and standard error s if for every pair of numbers a <= b, the chance that a < (X-m)/s < b is P(a < (X-m)/s < b) = area under the normal curve between a and b If there are numbers m and s such that X has a normal distribution with mean m and standard error s, then X is said to have a normal distribution or to be normally distributed If X has a normal distribution with mean m=0 and standard error s=1, then X is said to have a standard normal distribution The notation X~N(m,s2) means that X has a normal distribution with mean m and standard error s; for example, X~N(0,1), means X has a standard normal distribution; Any of a family of bell-shaped frequency curves whose relative position and shape are defined on the basis of the mean and standard deviation; a theoretical distribution with finite mean and variance; graph which shows the majority of a population to be concentrated around the average and fewer people located at the extremes, bell curve graph; The mathematical function that describes the symmetric bell-shaped curve defined by the Gaussian; Based on a mathematical formula, the normal distribution is theoretically symmetrical and bell shaped Random events such as height, running speed, and IQ scores tend to fall across this distribution While no empirical distribution of scores fulfills all of the requirements of the normal distribution, many carefully defined tests approximate this distribution closely enough to make use of some of the principles of the distribution This includes the fact that 68% of scores fall between one standard deviation above and below the mean (e g with NCE's - between 29 and 71 nationally) The 16th percentile lies one standard deviation below the mean and the 84th percentile lies one standard deviation above the mean in normally distributed scores Measures of central tendency such as the mean, median, and mode are the same in a normal distribution (50 with NCE's); A symmetric bell-shaped frequency distribution that is the approximate sampling distribution for many statistical estimates The normal distribution is completely determined by its mean and standard deviation The standard normal distribution has a mean of (0) and a standard deviation of (1); A probability distribution forming a symmetrical bell-shaped curve; A theoretical distribution of scores which forms a curve that is bell shaped and symmetrical; The usual "bell shaped" distribution which may or may not be due to Carl Friedrich Gauss 1777-1855 Called "normal" because it is similar to many real-world distributions Note that real-world distributions can be similar to normal, and still differ from it in serious systematic ways Also see the normal computation page "The" normal distribution is in fact a family of distributions, as parameterized by mean and standard deviation values By computing the sample mean and standard deviation, we can reduce the whole family into a single curve A value from any normal-like distribution can be "normalized" by subtracting the mean and dividing by the standard deviation; the result can be used to look up probabilities in standard normal tables All of which of course assumes that the underlying distribution is in fact normal, which may or may not be the case; A theoretical frequency distribution for a set of variable data, usually represented by a bell-shaped curve symmetrical about the mean. Also called Gaussian distribution. In statistics, a frequency distribution in the shape of the classic bell curve. It accurately represents most variations in such attributes as height and weight. Any random variable with a normal distribution has a mean (see mean, median, and mode) and a standard deviation that indicates how much the data as a whole deviate from the mean. The standard deviation is smaller for data clustered closely around the mean value and larger for more dispersed data sets; The frequency of a data distribution simulating a bell-shaped curve that is symmetrical around the mean and exhibits an equal chance of a data point being above or below the mean (syn: Gaussian distribution); The fundamental frequency distribution of statistical analysis A continuous variety x is said to have a normal distribution or to be normally distributed if it possesses a density function f(x) which satisfies the equation where μ is the arithmetic mean (or first moment) and σ is the standard deviation Also called Gaussian distribution; a continuous distribution that is bell shaped and symmetrical about the mean; Gaussian distribution
standard normal distribution: The normal distribution with a mean of zero and a variance of one