A random process is a sequence of random variables defined over a period of time.
A random variate is a particular outcome of a random variable: the random variates which are other outcomes of the same random variable would have different values.
· A variable whose values are determined by the outcomes of a random experiment is called a random variable. A random variable is a discrete random variable if it can assume values, which are finite or countable infinite. For example, tossing of a die is a random experiment and its outcomes 1, 2, 3, 4, 5 and 6 are discrete random variable. When a coin is tossed, its outcomes head and tail are discrete random variable. Three coins are thrown; the number of heads is example of discrete random variable. Note that the outcomes need ot be integers or even numbers (eg colour of eyes). · If a random variable can assume every possible value in an interval [a, b], a< b, where a and b may be - infinity and + infinity respectively, for example, the points on number line between 0 and 1; Value of 'x' between 0 and 2; Number of heads on a coin when it is tossed infinite times.
The value of a random variable that is uniformly distributed between 20 and 100 can be calculated by calculating the sum of numbers from 20 to 100 and dividing it by the difference between 100 and 20. The resulting mean is 58.5.
A random number is also called a random deviate or pseudo random number. It is a value that is generated by a computer, given a probability distribution. The uniform random number is distributed according to the uniform distribution with values from 0 to 1 and is often used to generate other numbers that follow other distributions. The relative frequency plot of random numbers generated using a particular distribution may not be exactly equal to the particular distribution, due to the limited number of points. But, as the number of random deviates increases, the frequency plot will more closely approximate the given distribution. Random variable have both an intuitive and mathematical definitions. Probability theory is based on sets of events. A throw of coin will have certain outcomes. The random variable, X, links or maps these events to values. A coin can come up heads, so the mapped random variable of this outcome can be 0, and for tails, the random variable of this outcome can be 1. The mathematical definition is a bit more complex- see related links. A random variable is associated with what is considered a random process. If we know the outcome without any uncertainty, we would call it an deterministic process and the outcomes deterministic variables.
A random process is a sequence of random variables defined over a period of time.
Random variables is a function that can produce outcomes with different probability and random variates is the particular outcome of a random variable.
A random variable is a variable that can take different values according to a process, at least part of which is random.For a discrete random variable (RV), a probability distribution is a function that assigns, to each value of the RV, the probability that the RV takes that value.The probability of a continuous RV taking any specificvalue is always 0 and the distribution is a density function such that the probability of the RV taking a value between x and y is the area under the distribution function between x and y.
A random variate is a particular outcome of a random variable: the random variates which are other outcomes of the same random variable would have different values.
· A variable whose values are determined by the outcomes of a random experiment is called a random variable. A random variable is a discrete random variable if it can assume values, which are finite or countable infinite. For example, tossing of a die is a random experiment and its outcomes 1, 2, 3, 4, 5 and 6 are discrete random variable. When a coin is tossed, its outcomes head and tail are discrete random variable. Three coins are thrown; the number of heads is example of discrete random variable. Note that the outcomes need ot be integers or even numbers (eg colour of eyes). · If a random variable can assume every possible value in an interval [a, b], a< b, where a and b may be - infinity and + infinity respectively, for example, the points on number line between 0 and 1; Value of 'x' between 0 and 2; Number of heads on a coin when it is tossed infinite times.
The value of a random variable that is uniformly distributed between 20 and 100 can be calculated by calculating the sum of numbers from 20 to 100 and dividing it by the difference between 100 and 20. The resulting mean is 58.5.
It is a discrete random variable.
When it is random it is variable.
It depends on what the random variable is, what its domain is, what its probability distribution function is. The probability that a randomly selected random variable has a value between 40 and 60 is probably quite close to zero.
A random number is also called a random deviate or pseudo random number. It is a value that is generated by a computer, given a probability distribution. The uniform random number is distributed according to the uniform distribution with values from 0 to 1 and is often used to generate other numbers that follow other distributions. The relative frequency plot of random numbers generated using a particular distribution may not be exactly equal to the particular distribution, due to the limited number of points. But, as the number of random deviates increases, the frequency plot will more closely approximate the given distribution. Random variable have both an intuitive and mathematical definitions. Probability theory is based on sets of events. A throw of coin will have certain outcomes. The random variable, X, links or maps these events to values. A coin can come up heads, so the mapped random variable of this outcome can be 0, and for tails, the random variable of this outcome can be 1. The mathematical definition is a bit more complex- see related links. A random variable is associated with what is considered a random process. If we know the outcome without any uncertainty, we would call it an deterministic process and the outcomes deterministic variables.
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A discrete random variable is a variable that can only take some selected values. The values that it can take may be infinite in number (eg the counting numbers), but unlike a continuous random variable, it cannot take any value in between valid results.