TABLES. Cumulative normal distribution. Critical values of the t distribution. Critical These tables have been computed to accompany the text C. Dougherty . STANDARD NORMAL DISTRIBUTION TABLE. Entries represent Pr(Z ≤ z). The value of z to the first decimal is given in the left column. The second decimal is. Tables of the Normal Cumulative Distribution. The table below gives the probability p that a Standard Normal random variable Z (ie mean = 0 and variance = 1).

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STANDARD NORMAL DISTRIBUTION: Table Values Represent AREA to the LEFT of the Z score. Z Standard Normal (Z) Table. Area between 0 and z. Standard Normal Distribution Table. 0 z z

Mathematics of Computation. The following auxiliary formula is useful for simplifying the posterior update equations, which otherwise become fairly tedious. Annals of Mathematical Statistics Gauss himself apparently coined the term with reference to the "normal equations" involved in its applications, with normal having its technical meaning of orthogonal rather than "usual". West, Graeme

The normal distribution is a subclass of the elliptical distributions. The normal distribution is symmetric about its mean, and is non-zero over the entire real line.

As such it may not be a suitable model for variables that are inherently positive or strongly skewed, such as the weight of a person or the price of a share. Such variables may be better described by other distributions, such as the log-normal distribution or the Pareto distribution. Therefore, it may not be an appropriate model when one expects a significant fraction of outliers —values that lie many standard deviations away from the mean—and least squares and other statistical inference methods that are optimal for normally distributed variables often become highly unreliable when applied to such data.

In those cases, a more heavy-tailed distribution should be assumed and the appropriate robust statistical inference methods applied. The Gaussian distribution belongs to the family of stable distributions which are the attractors of sums of independent, identically distributed distributions whether or not the mean or variance is finite.

Except for the Gaussian which is a limiting case, all stable distributions have heavy tails and infinite variance. Here n! The central absolute moments coincide with plain moments for all even orders, but are nonzero for odd orders. See also generalized Hermite polynomials.

The cumulant generating function is the logarithm of the moment generating function, namely. These integrals cannot be expressed in terms of elementary functions, and are often said to be special functions. However, many numerical approximations are known; see below. Its antiderivative indefinite integral is. The CDF of the standard normal distribution can be expanded by Integration by parts into a series:.

An asymptotic expansion of the CDF for large x can also be derived using integration by parts; see Error function Asymptotic expansion. This fact is known as the The quantile function of a distribution is the inverse of the cumulative distribution function.

The quantile function of the standard normal distribution is called the probit function , and can be expressed in terms of the inverse error function:. These values are used in hypothesis testing , construction of confidence intervals and Q-Q plots. In particular, the quantile z 0. These values are useful to determine tolerance interval for sample averages and other statistical estimators with normal or asymptotically normal distributions:. The central limit theorem states that under certain fairly common conditions, the sum of many random variables will have an approximately normal distribution.

Many test statistics , scores , and estimators encountered in practice contain sums of certain random variables in them, and even more estimators can be represented as sums of random variables through the use of influence functions.

The central limit theorem implies that those statistical parameters will have asymptotically normal distributions. The central limit theorem also implies that certain distributions can be approximated by the normal distribution, for example:. Whether these approximations are sufficiently accurate depends on the purpose for which they are needed, and the rate of convergence to the normal distribution. It is typically the case that such approximations are less accurate in the tails of the distribution.

A general upper bound for the approximation error in the central limit theorem is given by the Berry—Esseen theorem , improvements of the approximation are given by the Edgeworth expansions.

This functional can be maximized, subject to the constraints that the distribution is properly normalized and has a specified variance, by using variational calculus. A function with two Lagrange multipliers is defined:. The family of normal distributions is closed under linear transformations: This is a special case of the polarization identity.

More generally, any linear combination of independent normal deviates is a normal deviate. This property is called infinite divisibility.

More generally, if X 1 , The Hellinger distance between the same distributions is equal to.

If X 1 and X 2 are two independent standard normal random variables with mean 0 and variance 1, then. The split normal distribution is most directly defined in terms of joining scaled sections of the density functions of different normal distributions and rescaling the density to integrate to one.

The truncated normal distribution results from rescaling a section of a single density function. The notion of normal distribution, being one of the most important distributions in probability theory, has been extended far beyond the standard framework of the univariate that is one-dimensional case Case 1.

All these extensions are also called normal or Gaussian laws, so a certain ambiguity in names exists. The mean, variance and third central moment of this distribution have been determined [46]. One of the main practical uses of the Gaussian law is to model the empirical distributions of many different random variables encountered in practice. In such case a possible extension would be a richer family of distributions, having more than two parameters and therefore being able to fit the empirical distribution more accurately.

The examples of such extensions are:. Many tests over 40 have been devised for this problem, the more prominent of them are outlined below:. It is often the case that we don't know the parameters of the normal distribution, but instead want to estimate them. That is, having a sample x 1 , The standard approach to this problem is the maximum likelihood method, which requires maximization of the log-likelihood function:.

This implies that the estimator is finite-sample efficient. This fact is widely used in determining sample sizes for opinion polls and the number of trials in Monte Carlo simulations.

The estimator is also asymptotically normal , which is a simple corollary of the fact that it is normal in finite samples:. This other estimator is denoted s 2 , and is also called the sample variance , which represents a certain ambiguity in terminology; its square root s is called the sample standard deviation.

The two estimators are also both asymptotically normal:. There is also a converse theorem: Bayesian analysis of normally distributed data is complicated by the many different possibilities that may be considered:. The formulas for the non-linear-regression cases are summarized in the conjugate prior article. The following auxiliary formula is useful for simplifying the posterior update equations, which otherwise become fairly tedious. This equation rewrites the sum of two quadratics in x by expanding the squares, grouping the terms in x , and completing the square.

Note the following about the complex constant factors attached to some of the terms:. A similar formula can be written for the sum of two vector quadratics: In other words, it sums up all possible combinations of products of pairs of elements from x , with a separate coefficient for each.

For a set of i. This can be shown more easily by rewriting the variance as the precision , i. First, the likelihood function is using the formula above for the sum of differences from the mean:. This can be written as a set of Bayesian update equations for the posterior parameters in terms of the prior parameters:. This makes logical sense if the precision is thought of as indicating the certainty of the observations: In the distribution of the posterior mean, each of the input components is weighted by its certainty, and the certainty of this distribution is the sum of the individual certainties.

For the intuition of this, compare the expression "the whole is or is not greater than the sum of its parts". In addition, consider that the knowledge of the posterior comes from a combination of the knowledge of the prior and likelihood, so it makes sense that we are more certain of it than of either of its components. The above formula reveals why it is more convenient to do Bayesian analysis of conjugate priors for the normal distribution in terms of the precision.

The posterior precision is simply the sum of the prior and likelihood precisions, and the posterior mean is computed through a precision-weighted average, as described above. The same formulas can be written in terms of variance by reciprocating all the precisions, yielding the more ugly formulas. The two are equivalent except for having different parameterizations. Although the inverse gamma is more commonly used, we use the scaled inverse chi-squared for the sake of convenience.

The likelihood function from above, written in terms of the variance, is:. Reparameterizing in terms of an inverse gamma distribution , the result is:. Logically, this originates as follows:. The respective numbers of pseudo-observations add the number of actual observations to them.

The new mean hyperparameter is once again a weighted average, this time weighted by the relative numbers of observations. The likelihood function from the section above with known variance is:. The occurrence of normal distribution in practical problems can be loosely classified into four categories:.

Certain quantities in physics are distributed normally, as was first demonstrated by James Clerk Maxwell. Examples of such quantities are:. Approximately normal distributions occur in many situations, as explained by the central limit theorem. When the outcome is produced by many small effects acting additively and independently , its distribution will be close to normal.

The normal approximation will not be valid if the effects act multiplicatively instead of additively , or if there is a single external influence that has a considerably larger magnitude than the rest of the effects. I can only recognize the occurrence of the normal curve — the Laplacian curve of errors — as a very abnormal phenomenon.

It is roughly approximated to in certain distributions; for this reason, and on account for its beautiful simplicity, we may, perhaps, use it as a first approximation, particularly in theoretical investigations. There are statistical methods to empirically test that assumption, see the above Normality tests section. In regression analysis , lack of normality in residuals simply indicates that the model postulated is inadequate in accounting for the tendency in the data and needs to be augmented; in other words, normality in residuals can always be achieved given a properly constructed model.

In computer simulations, especially in applications of the Monte-Carlo method , it is often desirable to generate values that are normally distributed. All these algorithms rely on the availability of a random number generator U capable of producing uniform random variates. The standard normal CDF is widely used in scientific and statistical computing. Different approximations are used depending on the desired level of accuracy.

Shore introduced simple approximations that may be incorporated in stochastic optimization models of engineering and operations research, like reliability engineering and inventory analysis. This approximation delivers for z a maximum absolute error of 0. Another approximation, somewhat less accurate, is the single-parameter approximation:. The latter had served to derive a simple approximation for the loss integral of the normal distribution, defined by.

Some more approximations can be found at: Error function Approximation with elementary functions. In Gauss published his monograph " Theoria motus corporum coelestium in sectionibus conicis solem ambientium " where among other things he introduces several important statistical concepts, such as the method of least squares , the method of maximum likelihood , and the normal distribution. Using this normal law as a generic model for errors in the experiments, Gauss formulates what is now known as the non-linear weighted least squares NWLS method.

Although Gauss was the first to suggest the normal distribution law, Laplace made significant contributions. It is of interest to note that in an American mathematician Adrain published two derivations of the normal probability law, simultaneously and independently from Gauss. In the middle of the 19th century Maxwell demonstrated that the normal distribution is not just a convenient mathematical tool, but may also occur in natural phenomena: Since its introduction, the normal distribution has been known by many different names: Gauss himself apparently coined the term with reference to the "normal equations" involved in its applications, with normal having its technical meaning of orthogonal rather than "usual".

Peirce one of those authors once defined "normal" thus: Many years ago I called the Laplace—Gaussian curve the normal curve, which name, while it avoids an international question of priority, has the disadvantage of leading people to believe that all other distributions of frequency are in one sense or another 'abnormal'.

Soon after this, in year , Fisher added the location parameter to the formula for normal distribution, expressing it in the way it is written nowadays:. The term "standard normal", which denotes the normal distribution with zero mean and unit variance came into general use around the s, appearing in the popular textbooks by P. Hoel " Introduction to mathematical statistics " and A.

Mood " Introduction to the theory of statistics ". When the name is used, the "Gaussian distribution" was named after Carl Friedrich Gauss , who introduced the distribution in as a way of rationalizing the method of least squares as outlined above.

Among English speakers, both "normal distribution" and "Gaussian distribution" are in common use, with different terms preferred by different communities. From Wikipedia, the free encyclopedia. This article is about the univariate normal distribution. For normally distributed vectors, see Multivariate normal distribution. For normally distributed matrices, see Matrix normal distribution. For other uses, see Bell curve disambiguation. See also: List of integrals of Gaussian functions.

Further information: Interval estimation and Coverage probability. Main article: Central limit theorem. If X and Y are jointly normal and uncorrelated , then they are independent.

The requirement that X and Y should be jointly normal is essential; without it the property does not hold. Normality tests. Standard error of the mean. Hart lists some dozens of approximations — by means of rational functions, with or without exponentials — for the erfc function. His algorithms vary in the degree of complexity and the resulting precision, with maximum absolute precision of 24 digits. An algorithm by West combines Hart's algorithm with a continued fraction approximation in the tail to provide a fast computation algorithm with a digit precision.

Cody after recalling Hart68 solution is not suited for erf, gives a solution for both erf and erfc, with maximal relative error bound, via Rational Chebyshev Approximation. Statistics portal.

But it was not until the year that he made his results publicly available. The original pamphlet was reprinted several times, see for example Walker Why are Normal Distributions Normal? Elements of Information Theory. John Wiley and Sons. Journal of Econometrics. Retrieved Supplement to the Journal of the Royal Statistical Society 3 2: Annals of Mathematical Statistics Tel Aviv University.

Archived from the original PDF on March 25, Applied Mathematics Series. Washington D. Computational Knowledge Engine".

If you use R or Excel you can get the thing for whatever value you need. I apologise for the misuse of language. I understand there are better ways to go about this than the table, however for completely benign reasons I did need the table, and I'm not savvy enough to generate it myself. Thanks for the time you've put in. Henry Henry Sign up or log in Sign up using Google.

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