# continuous random variable formula

We'll do that using a probability density function ("p.d.f."). If the theoretical and sample quantiles "match," there is good evidence that the data follow the supposed probability distribution. For a discrete random variable \(X\) that takes on a finite or countably infinite number of possible values, we determined \(P(X=x)\) for all of the possible values of \(X\), and called it the probability mass function ("p.m.f."). First, let's review why randomization is a useful venture when conducting an experiment. Expected value or Mathematical Expectation or Expectation of a random variable may be defined as the sum of products of the different values taken by the random variable and the corresponding probabilities. The area under the curve \(f(x)\) in the support \(S\) is 1, that is: If \(f(x)\) is the p.d.f. Finding the probability that \(X\) falls in some interval, that is finding \(P(a

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