You probably recall from the Discrete Probability Distributions section of this course that a discrete
random variable assumes values that are separate and distinct. The
probability distribution for a discrete random variable, such as the
number of orders taken in one week, may look like the following graph.
Alternatively, a continuous random variable can assume a range
of values that falls along a continuum. The probability distribution
for a continuous random variable can be represented by a curve that
spans the range of values that the variable can assume. The curve is
continuous and will not have distinct segments as in the discrete
Consider the continuous probability distribution below, which
illustrates the range of temperatures at which auto parts fractures
occur. The distribution shows that fractures occur between
approximately –45 and –15 degrees Fahrenheit.
A continuous probability distribution can be used to determine the
probability of a variable falling between any two chosen values within
the range of the distribution. The automobile manufacturer uses the
graph below to analyze the probability of the temperature being between
–35 and –30 degrees Fahrenheit when parts fractures occur, which is
indicated by the shaded area under the curve.
Integral calculus is used to find this area and calculate the probability, but that is beyond the scope of this course.
Consider a second business example: Each week, a manufacturer of
plastic products generates a varying amount of waste during production.
In order to maintain production schedules and avoid problems of
underutilized plant capacity, the manufacturer must have a sufficient
amount of raw material at its facility to compensate for the waste
generated during production.
The company's operations executive needs to know the amount of waste
generated each week. He will use this information to predict the
probability associated with various levels of waste. This will allow
him to determine the correct level of raw materials to have on hand for
a production run.
The operations executive uses past data to construct a probability
density function of the amount of waste generated each week. This
distribution is illustrated below.
Using this probability distribution, the executive can see that the
amount of waste ranges from 0 to 2,200 pounds, with the most probable
amount of waste being approximately 1,100 pounds.
If the executive wanted to determine the probability that the level
of waste will be between 800 and 1,400 pounds, he could calculate the
area under the curve between 800 and 1,400. This area under the curve
that he wishes to calculate is indicated by the shaded portion in the
Notice that the two continuous probability distributions you've seen
here have similar shapes. Both are approximations of a useful
distribution called the normal distribution, which will be examined in
detail in the Normal Distribution portion of this course.
1. Classify each of the following random variables as discrete or continuous.
a. the number of people waiting in line at a checkout counter
b. the size of an intake valve on a 1965 Ford Mustang
c. the color of a macaroni-and-cheese dinner
d. the time it takes a customer to choose a product
2. How is it possible to measure the probability of temperatures,
weights, times, or levels of satisfaction if there are an infinite
number of values that the variable can take on?
3. Is it possible for a continuous random variable to have a binomial distribution? Why or why not?