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(Dilbilim) normal distribution
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
düzgün sönümlü dağılım
tapered distribution
düzgün dağılım
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