- n.

- a constant in the equation of a curve that can be varied to yield a family of similar curves (同)parametric quantity
- a quantity (such as the mean or variance) that characterizes a statistical population and that can be estimated by calculations from sample data
- any factor that defines a system and determines (or limits) its performance
- be a contributing factor; "make things factor into a company
*s profitability"* - any of the numbers (or symbols) that form a product when multiplied together
- an independent variable in statistics
- anything that contributes causally to a result; "a number of factors determined the outcome"
- consider as relevant when making a decision; "You must factor in the recent developments" (同)factor in, factor out
- resolve into factors; "a quantum computer can factor the number 15" (同)factor in, factor out
- of or relating to factorials
- the product of all the integers up to and including a given integer; "1, 2, 6, 24, and 120 are factorials"

- (数学で)媒介変数,補助変数 / (統計で)母数
- (…の)『要因』,(…を生み出す)要素《+『in』+『名』(do『ing』)》 / 囲数,約数 / 代理人,《おもに英》仲買人 / =factorize
- (数学で)階乗 / 階乗の;因数の

出典(authority):フリー百科事典『ウィキペディア（Wikipedia）』「2014/11/13 12:16:37」(JST)

For other uses, see Parameter (disambiguation).

This article needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. (August 2011) |

A **parameter** (from the Ancient Greek παρά, "para", meaning "beside, subsidiary" and μέτρον, "metron", meaning "measure"), in its common meaning, is a characteristic, feature, or measurable factor that can help in defining a particular system. A parameter is an important element to consider in evaluation or comprehension of an event, project, or situation. *Parameter* has more specific interpretations in mathematics, logic, linguistics, environmental science,^{[1]} and other disciplines.

- 1 Mathematical functions
- 1.1 Parametric equations
- 1.2 Examples
- 1.3 Mathematical models
- 1.4 Analytic geometry
- 1.5 Mathematical analysis
- 1.6 Statistics and econometrics
- 1.7 Probability theory

- 2 Computing
- 3 Computer programming
- 4 Engineering
- 5 Environmental science
- 6 Linguistics
- 7 Logic
- 8 See also
- 9 References

Mathematical functions have one or more arguments that are designated in the definition by variables. A function definition can also contain parameters, but unlike variables, parameters are not listed among the arguments that the function takes. When parameters are present, the definition actually defines a whole family of functions, one for every valid set of values of the parameters. For instance, one could define a general quadratic function by defining

- ;

here, the variable *x* designates the function's argument, but *a*, *b*, and *c* are parameters that determine which particular quadratic function is being considered. A parameter could be incorporated into the function name to indicate its dependence on the parameter. For instance, one may define the base *b* of a logarithm by

where *b* is a parameter that indicates which logarithmic function is being used. It is not an argument of the function, and will, for instance, be a constant when considering the derivative .

In some informal situations it is a matter of convention (or historical accident) whether some or all of the symbols in a function definition are called parameters. However, changing the status of symbols between parameter and variable changes the function as a mathematical object. For instance, the notation for the falling factorial power

- ,

defines a polynomial function of *n* (when *k* is considered a parameter), but is not a polynomial function of *k* (when *n* is considered a parameter). Indeed, in the latter case, it is only defined for non-negative integer arguments. More formal presentations of such situations typically start out with a function of several variables (including all those that might sometimes be called "parameters") such as

as the most fundamental object being considered, then defining functions with fewer variables from the main one by means of currying.

Sometimes it is useful to consider all functions with certain parameters as *parametric family*, i.e. as an indexed family of functions. Examples from probability theory are given further below.

In the special case of parametric equations, the independent variables are called the parameters. This is effectively a separate meaning of the word, although it arises as a degenerate case of the main one.

- In a section on frequently misused words in his book
*The Writer's Art*, James J. Kilpatrick quoted a letter from a correspondent, giving examples to illustrate the correct use of the word*parameter*:

“ | W.M. Woods ... a mathematician ... writes ... "... a variable is one of the many things a parameter is not." ... The dependent variable, the speed of the car, depends on the independent variable, the position of the gas pedal. |
” |

“ | [Kilpatrick quoting Woods] "Now ... the engineers ... change the lever arms of the linkage ... the speed of the car ... will still depend on the pedal position ... but in a ... different manner. You have changed a parameter" |
” |

- A parametric equaliser is an audio filter that allows the frequency of maximum cut or boost to be set by one control, and the size of the cut or boost by another. These settings, the frequency level of the peak or trough, are two of the parameters of a frequency response curve, and in a two-control equaliser they completely describe the curve. More elaborate parametric equalisers may allow other parameters to be varied, such as skew. These parameters each describe some aspect of the response curve seen as a whole, over all frequencies. A graphic equaliser provides individual level controls for various frequency bands, each of which acts only on that particular frequency band.

- If asked to imagine the graph of the relationship
*y*=*ax*^{2}, one typically visualizes a range of values of*x*, but only one value of*a*. Of course a different value of*a*can be used, generating a different relation between*x*and*y*. Thus*a*is a parameter: it is less variable than the variable*x*or*y*, but it is not an explicit constant like the exponent 2. More precisely, changing the parameter*a*gives a different (though related) problem, whereas the variations of the variables*x*and*y*(and their interrelation) are part of the problem itself.

- In calculating income based on wage and hours worked (income equals wage multiplied by hours worked), it is typically assumed that the number of hours worked is easily changed, but the wage is more static. This makes 'wage' a
*parameter*, 'hours worked' an*independent variable*, and 'income' a*dependent variable*.

In the context of a mathematical model, such as a probability distribution, the distinction between variables and parameters was described by Bard as follows:

- We refer to the relations which supposedly describe a certain physical situation, as a
*model*. Typically, a model consists of one or more equations. The quantities appearing in the equations we classify into*variables*and*parameters*. The distinction between these is not always clear cut, and it frequently depends on the context in which the variables appear. Usually a model is designed to explain the relationships that exist among quantities which can be measured independently in an experiment; these are the variables of the model. To formulate these relationships, however, one frequently introduces "constants" which stand for inherent properties of nature (or of the materials and equipment used in a given experiment). These are the parameters.^{[2]}

In analytic geometry, curves are often given as the image of some function. The argument of the function is invariably called "the parameter". A circle of radius 1 centered at the origin can be specified in more than one form:

*implicit*form

*parametric*form

- where
*t*is the*parameter*.

A somewhat more detailed description can be found at parametric equation.

In mathematical analysis, integrals dependent on a parameter are often considered. These are of the form

In this formula, *t* is the argument of the function *F*, and on the right-hand side the *parameter* on which the integral depends. When evaluating the integral, *t* is held constant, and so it is considered a parameter. If we are interested in the value of *F* for different values of *t*, we now consider it a variable. The quantity *x* is a *dummy variable* or *variable of integration* (confusingly, also sometimes called a *parameter of integration*).

In statistics and econometrics, the probability framework above still holds, but attention shifts to estimating the parameters of a distribution based on observed data, or testing hypotheses about them. In classical estimation these parameters are considered "fixed but unknown", but in Bayesian estimation they are treated as random variables, and their uncertainty is described as a distribution.^{[citation needed]}

A statistic is a numerical characteristic of a sample that can be used as an estimate of the corresponding parameter, the numerical characteristic of the population from which the sample was drawn. For example, the sample mean (usually denoted ) can be used as an estimate of the *mean* parameter (μ) of the population from which the sample was drawn.

It is possible to make statistical inferences without assuming a particular parametric family of probability distributions. In that case, one speaks of *non-parametric statistics* as opposed to the parametric statistics just described. For example, a test based on Spearman's rank correlation coefficient would be called non-parametric since the statistic is computed from the rank-order of the data disregarding their actual values (and thus regardless of the distribution they were sampled from), whereas those based on the Pearson product-moment correlation coefficient are parametric tests since it is computed directly from the data values and thus estimates the parameter known as the population correlation.

In probability theory, one may describe the distribution of a random variable as belonging to a *family* of probability distributions, distinguished from each other by the values of a finite number of *parameters*. For example, one talks about "a Poisson distribution with mean value λ". The function defining the distribution (the probability mass function) is:

This example nicely illustrates the distinction between constants, parameters, and variables. *e* is Euler's number, a fundamental mathematical constant. The parameter λ is the mean number of observations of some phenomenon in question, a property characteristic of the system. *k* is a variable, in this case the number of occurrences of the phenomenon actually observed from a particular sample. If we want to know the probability of observing *k _{1}* occurrences, we plug it into the function to get . Without altering the system, we can take multiple samples, which will have a range of values of

For instance, suppose we have a radioactive sample that emits, on average, five particles every ten minutes. We take measurements of how many particles the sample emits over ten-minute periods. The measurements exhibit different values of *k*, and if the sample behaves according to Poisson statistics, then each value of *k* will come up in a proportion given by the probability mass function above. From measurement to measurement, however, λ remains constant at 5. If we do not alter the system, then the parameter λ is unchanged from measurement to measurement; if, on the other hand, we modulate the system by replacing the sample with a more radioactive one, then the parameter λ would increase.

Another common distribution is the normal distribution, which has as parameters the mean μ and the variance σ².

In these above examples, the distributions of the random variables are completely specified by the type of distribution, i.e. Poisson or normal, and the parameter values, i.e. mean and variance. In such a case, we have a parameterized distribution.

It is possible to use the sequence of moments (mean, mean square, ...) or cumulants (mean, variance, ...) as parameters for a probability distribution: see Statistical parameter.

In computing a parameter is defined as "a reference or value that is passed to a function, procedure, subroutine, command, or program."^{[3]} For example, a program may be passed the name of a file on which it should perform some function.

Main article: Parameter (computer programming)

In computer programming two notions of parameter are commonly used, and referred to as parameters and arguments, or more formally (in computer science) as a **formal parameter** and an **actual parameter**. In casual usage these are sometimes not distinguished, and the terms *parameter* and *argument* are incorrectly used interchangeably.

In the definition of a function such as

*f*(*x*) =*x*+ 2,

*x* is a *formal parameter* (*parameter*). When the function is evaluated, as in

*f*(3) or*y*=*f*(3) + 5,

3 is the *actual parameter* (*argument*): the value that is substituted for the *formal parameter* used in the function definition.

These concepts are discussed in a more precise way in functional programming and its foundational disciplines, lambda calculus and combinatory logic. Terminology varies between languages: some computer languages such as C define parameter and argument as given here, while Eiffel for instance uses an alternative convention.

In engineering (especially involving data acquisition) the term *parameter* sometimes loosely refers to an individual measured item. This usage isn't consistent, as sometimes the term *channel* refers to an individual measured item, with *parameter* referring to the setup information about that channel.

"Speaking generally, **properties** are those physical quantities which directly describe the physical attributes of the system; **parameters** are those combinations of the properties which suffice to determine the response of the system. Properties can have all sorts of dimensions, depending upon the system being considered; parameters are dimensionless, or have the dimension of time or its reciprocal."^{[4]}

The term can also be used in engineering contexts, however, as it is typically used in the physical sciences.

In environmental science and particularly in chemistry and microbiology, a parameter is used to describe a discrete chemical or microbiological entity which can be assigned a value which is commonly a concentration. The value may also be a logical entity (present or absent), a statistical result such as a 95%ile value or in some cases a subjective value

Within linguistics, the word "parameter" is almost exclusively used to denote a binary switch in a Universal Grammar within a Principles and Parameters framework.

In logic, the parameters passed to (or operated on by) an *open predicate* are called *parameters* by some authors (e.g., Prawitz, "Natural Deduction"; Paulson, "Designing a theorem prover"). Parameters locally defined within the predicate are called *variables*. This extra distinction pays off when defining substitution (without this distinction special provision must be made to avoid variable capture). Others (maybe most) just call parameters passed to (or operated on by) an open predicate *variables*, and when defining substitution have to distinguish between *free variables* and *bound variables*.

- One-parameter group
- Parametrization (i.e., coordinate system)
- Parametrization (climate)
- Parsimony (with regards to the trade-off of many or few parameters in data fitting)

**^**Parameter from the Free Dictionary**^**Bard, Yonathan (1974).*Nonlinear Parameter Estimation*. New York: Academic Press. p. 11. ISBN 0-12-078250-2.**^**"The Free Dictionary: Parameter". Retrieved March 3, 2014.**^**Trimmer, John D. (1950).*Response of Physical Systems*. New York: Wiley. p. 13.

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リンク元 | 「要因」「factorial」「factor」「助変数」「限定要素」 |

拡張検索 | 「pharmacokinetic parameter」 |

- n.

- 連乗積。階乗

- adj.

- 階乗の。因数の。(取り立て)代理業の。要因の。factoryの

- n.