STAT 2040 Lecture Notes - Lecture 6: Probability Distribution, Random Variable, Brain Size
3 Mar 2017
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Continuous random variables can take on an in nite number of possible values, correspond- ing to all values in an interval. (all values between 0 and 1) Common examples of continuous variables include heights, weights, and time to an event. e. g. let the random variable x represent the cranial capacity of a randomly selected adult robin in toronto. A continuous random variable has a continuous probability distribution. A continuous probability distribution is represented by a probability density function, f (x), which is a function (a curve) that gives the relative likelihood of all of the possible values of x. Figure 1 illustrates the distribution of a continuous random variable x, which represents the height of a randomly selected adult american female. a density function is for continuous random variables (the pdf) 0. 02 f(x) these values of x are more likely (where the height of the curve is higher) height is a continuous random variable.
