Module 5: Continuous Random Variables
Continuous Probability Functions
Barbara Illowsky & OpenStax et al.
We begin by defining a continuous probability density function. We use the function notation f(x). Intermediate algebra may have been your first formal introduction to functions. In the study of probability, the functions we study are special. We define the function f(x) so that the area between it and the x-axis is equal to a probability. Since the maximum probability is one, the maximum area is also one. For continuous probability distributions, PROBABILITY = AREA.
Example
Consider the function
The graph of
The area between
Suppose we want to find the area between
The area corresponds to the probability [latex]P(4
Suppose we want to find
Label the graph with f(x) and x. Scale the x and y axes with the maximum x and y values.f(x) =
To calculate the probability that x is between two values, look at the following graph. Shade the region between
Please watch this video to help you summarize what you just read.
Try It
Consider the function
[reveal-answer q=”287031″]Show Solution[/reveal-answer]
[hidden-answer a=”287031″]
[latex]P (2.5
Concept Review
The probability density function (pdf) is used to describe probabilities for continuous random variables. The area under the density curve between two points corresponds to the probability that the variable falls between those two values. In other words, the area under the density curve between points
a and b is equal to [latex]P(a
The cumulative distribution function (cdf) of X is defined by P (X ≤ x). It is a function of x that gives the probability that the random variable is less than or equal to x.
Formula Review
Probability density function (pdf) f(x):
- The total area under the curve f(x) is one.
Cumulative distribution function (cdf):