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The expected value in statistics is the long-run average outcome of a random variable based on its possible outcomes and their respective probabilities. Essentially, if an experiment (like a game of chance) were repeated, the expected value tells us the average result we’d see in the long run.
In probability theory, the expected value (also called expectation, expectancy, expectation operator, mathematical expectation, mean, expectation value, or first moment) is a generalization of the weighted average.
To find the expected value, E(X), or mean μ of a discrete random variable X, simply multiply each value of the random variable by its probability and add the products. The formula is given as E ( X ) = μ = ∑ x P ( x ) .
The basic expected value formula is the probability of an event multiplied by the amount of times the event happens: (P (x) * n). The formula changes slightly according to what kinds of events are happening.
Expected Value: If O O represents an outcome of an experiment and n (O) n (O) represents the value of that outcome, then the expected value of the experiment is: ∑ n ( O ) × P ( O ) ∑ n ( O ) × P ( O ) ,
1 lip 2020 · The expected value, or mean, of a discrete random variable predicts the long-term results of a statistical experiment that has been repeated many times. The standard deviation of a probability distribution is used to measure the variability of possible outcomes.
7 lut 2024 · Expected value formula. Mathematically speaking, the expected value of a random variable X X is the sum of each possible value x x of X X, multiplied by the probability of that value, P (x) P (x). Have a look at the expected value formula: \small E (X) = x_1 \cdot P (x_1) + \ldots + x_n \cdot P (x_n) E (X) = x1 ⋅ P (x1) + … + xn ⋅ P (xn)
