random variable

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ran·dom var·i·a·ble

a variable that may assume a set of values, each with fixed probabilities or probability densities (its distribution), in such a way that the total probability assigned to the distribution is unity; the random variable may be discrete, continuous, or mixed discrete-continuous.


unplanned, without direction or purpose.

random assignment
random mating
where each member of the population has an equal opportunity of mating with every member of the opposite sex.
random numbers
a list of numbers obtained by a standard randomization procedure; used commonly to select individual animals from a pack.
random sample
see random sample.
random sampling
a procedure for selecting units from a group in such a way that each unit has an equal chance of being selected in the sample.
random selection
selection in such a way as to produce a random sample.
random variable
a group or quantity that takes various values, each with varying probabilities.
References in periodicals archive ?
In a Monte Carlo simulation approach stochastic variables are those variables that the decision maker cannot foresee with certainty.
The stochastic variables used in the model developed in this study include the average weekly price of blueberries ([P.
The value of each of the K stochastic variables is simulated for the ith period where [x'.
Fully specifying the data generating process requires the analyst to specify likely ranges for the each of the K stochastic variables for both the subsequent period and the terminal period.
That means there may be no relationship between some stochastic variables.
The influence of individual stochastic variables to the results of calculations is further analysed.
The two key stochastic variables that importantly determine market risks in the insurance are stock return and interest rate.
42) Internal Revenue Code minimum contribution rules are programmed into the model so that underfunding is continually recalculated as a function of the stochastic variables, subject to the minimum contribution rules.
We assume in each case that firms are able to observe the realizations of the stochastic variables before making their production decisions.
The method of Balcombe and Smith (1999) requires the researcher to supply a correlation matrix for the stochastic variables as well as likely ranges for each of the K stochastic variables in both the subsequent period and the terminal period.
The process of simulating observations on the K stochastic variables and calculating the project's NPV must be repeated a sufficiently large number of times.
For demonstration purposes we will consider the example, where y = U1 + U2 + U3 with U's being stochastic variables.