Continuous Random Variables
... The graph of a continuous probability distribution is a curve. Probability is represented by area under the curve. The curve is called the probability density function (abbreviated: pdf). We use the symbol f ( x ) to represent the curve. f ( x ) is the function that corresponds to the graph; we use ...
... The graph of a continuous probability distribution is a curve. Probability is represented by area under the curve. The curve is called the probability density function (abbreviated: pdf). We use the symbol f ( x ) to represent the curve. f ( x ) is the function that corresponds to the graph; we use ...
Probabilities of Compound Events
... Compound Events: the union or intersection of two events Disjoint (Mutually Exclusive) Events: no outcomes in common Overlapping Events: one or more outcomes in common ...
... Compound Events: the union or intersection of two events Disjoint (Mutually Exclusive) Events: no outcomes in common Overlapping Events: one or more outcomes in common ...
1 Introduction to Random Variables
... Written this last way, we can figure out how to compute the mean1 of a discrete random variable. Imagine choosing one of our numbers {x1 , x2 , . . . xn } at random, each equally likely. Then n1 is the probability of choosing a particular number in our list. So we see that we have a sum of values ti ...
... Written this last way, we can figure out how to compute the mean1 of a discrete random variable. Imagine choosing one of our numbers {x1 , x2 , . . . xn } at random, each equally likely. Then n1 is the probability of choosing a particular number in our list. So we see that we have a sum of values ti ...