STAT 3005 Lecture Notes - Lecture 5: Simple Random Sample, Unimodality, Fair Coin
8 Feb 2018
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Document Summary
A probability model describes the possible outcomes of a chance process and the likelihood that those outcomes will occur. A numerical variable that describes the outcomes of a chance process is called a random variable. The probability for a random variable is its probability distribution. A random variable takes numerical values that describe the outcomes of some chance process. The probability distribution of a random variable gives its possible values and their probabilities: example: consider tossing a fair coin three times where x = the number of heads obtained. The result of counting outcomes rather than measuring them: example: golf score, number of days it rained, number of customers. Integer values within an interval standing in line. Density/mass function: a display of discrete distributions where the probability of an outcomes the height of density over that point. X represents the random variable of interest and p(x = x) = p(x) = the probability that x = x.
