Examples of using Random variable in English and their translations into Norwegian
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Ecclesiastic
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Ecclesiastic
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Computer
What is a random variable?
The random variable then takes values which are real numbers from the interval is 1⁄2.
Continuous Random Variable.
Information theory: Entropy is a measure of the uncertainty associated with a random variable.
I(X) is itself a random variable.
Let X be a random variable with finite expected value μ and finite non-zero variance σ2.
If the image is uncountably infinite then X{\displaystyle X}is called a continuous random variable.
If X is a purely discrete random variable, then it attains values x1, x2,….
One way to prove Chebyshev's inequality is to apply Markov's inequality to the random variable Y(X- μ)2 with a(kσ)2.
Theorem[edit] Let X be a random variable with unimodal distribution, mean μ and finite, non-zero variance σ2.
FDIST is calculated as FDIST=P(F<x), where F is a random variable that has an F distribution.
A random variable has a probability distribution, which specifies the probability of its values.
Let X{\displaystyle X} be a real-valued,continuous random variable and let Y X 2{\displaystyle Y=X^{2.
Mathematically, the random variable is interpreted as a function which maps the person to the person's height.
In an experiment a person may be chosen at random, and one random variable may be the person's height.
Elo's central assumption was that the chess performance of each player in each game is a normally distributed random variable.
One may then specifically refer to a random variable of type E{\displaystyle E}, or an E{\displaystyle E}-valued random variable.
In probability theory, Markov's inequality gives an upper bound for the probability that a non-negative function of a random variable is greater than or equal to some positive constant.
In that context, a random variable is understood as a function defined on a sample space whose outcomes are numerical values.
Suppose X1,…, Xn are independent realizations of the normally-distributed, random variable X, which has an expected value μ and variance σ2.
The term"random variable" in statistics is traditionally limited to the real-valued case E R{\displaystyle E=\mathbb{R.
Probabilistic statement[edit] Let X(integrable) be a random variable with finite expected value μ and finite non-zero variance σ2.
A random variable is defined as a function that maps the outcomes of an unpredictable process to numerical quantities, typically real numbers.
Markov's inequality states that for any real-valued random variable Y and any positive number a, we have Pr(|Y|> a)≤ E(|Y|)/a.
Any random variable can be described by its cumulative distribution function, which describes the probability that the random variable will be less than or equal to a certain value.
For example, suppose that X is a discrete random variable, and that Y is a continuous random variable.
We could represent these directions by North, West, East, South, Southeast, etc. However,it is commonly more convenient to map the sample space to a random variable which takes values which are real numbers.
FDIST is calculated as FDIST=P(F>x),where F is a random variable that has an F distribution with deg_freedom1 and deg_freedom2 degrees of freedom.
A new random variable Y can be defined by applying a real Borel measurable function g: R→ R{\displaystyle g\colon\mathbb{R}\rightarrow\mathbb{R}} to the outcomes of a real-valued random variable X{\displaystyle X.
DIST. RT is calculated as F. DIST. RT=P(F>x),where F is a random variable that has an F distribution with deg_freedom1 and deg_freedom2 degrees of freedom.