4.3: Exponential Distributions
A third type of probability distribution is an exponential distribution. When we discussed normal distributions, or standard distributions, we talked about the fact that these distributions used continuous data, so you could use standard distributions when talking about heights, ages, lengths, temperatures, and the like. The same types of data are used when discussing exponential distributions. Exponential distributions, contrary to standard distributions, deal more with rates or changes over time. For example, the length of time the battery in your car will last is an exponential distribution. The length of time is a continuous random variable. A continuous random variable is one that can form an infinite number of groupings. So time, for example, can be broken down into hours, minutes, seconds, milliseconds, and so on. Another example of an exponential distribution is the lifetime of a computer part. Different computer parts have different life spans, depending on their use (and abuse). The rate of decay of the computer part determines the shape of the exponential distribution.
Let’s look at the differences between the normal distribution curve, a binomial distribution histogram, and an exponential distribution. List some of the similarities and differences that you see in the figures below.
Notice that with the standard distribution and the exponential distribution curves, the data represents continuous variables. The data in the binomial distribution histogram, on the other hand, is discrete. Also, the curve for the standard distribution is symmetrical about the mean. In other words, if you draw a horizontal line through the center of the curve, the 2 halves of the standard distribution curve would be mirror images of each other. This symmetry does not exist for the exponential distribution curve (nor for the binomial distribution). Did you notice anything else?
Let’s look at some examples where the resulting graphs would show you an exponential distribution.
Example 15
ABC Computer Company is doing a quality control check on their newest core chip. They randomly chose 25 chips from a batch of 200 to test and examined them to see how long they would continuously run before failing. The following results were obtained:
Number of Chips  Hours to Failure 

8  1,000 
6  2,000 
4  3,000 
3  4,000 
2  5,000 
2  6,000 
What kind of data is represented in the table?
Solution:
In order to solve this problem, you need to graph it to see what it looks like. You can use graph paper or your calculator. Entering the data into the TI84 involves the following keystrokes. There are a number of them, because you have to enter the data into L1 and L2, and then plot the lists using STAT PLOT.
After you press \begin{align*}\boxed{\text{GRAPH}}\end{align*}
This curve looks somewhat like an exponential distribution curve, but let’s test it out. You can do this on the TI84 by pressing \begin{align*}\boxed{\text{STAT}}\end{align*}
Notice that the \begin{align*}r^2\end{align*}
You use regression to determine a rule that best explains the data you are observing. There is a standard quantitative measure of this best fit, known as the coefficient of determination (\begin{align*}r^2\end{align*}
You can even go a step further and graph the exponential regression curve on top of our plotted points. Follow the keystrokes below and test it out.
Note: It was not indicated that the data was in L1 and L2 when finding the exponential regression. This is because it is the default of the calculator. If you had used L2 and L3, you would have had to add this to your keystrokes.
Take a look at the formula that you used with the exponential regression calculation (ExpReg) above. The general formula was \begin{align*}y= ab^x\end{align*}
Example 16
Radioactive substances are measured using a GeigerMüller counter (or a Geiger counter for short). Robert was working in his lab measuring the count rate of a radioactive particle. He obtained the following data:
Time (hr)  Count (atoms) 

15  544 
12  272 
9  136 
6  68 
3  34 
1  17 
Is this data representative of an exponential distribution? If so, find the equation. What would be the count at 7.5 hours?
Solution:
Remember, we can plot this data using pencil and paper, or we can use a graphing calculator. We will use a graphing calculator here.
The resulting graph appears as follows:
At a glance, it does look like an exponential curve, but we really have to take a closer look by doing the exponential regression.
In the analysis of the exponential regression, we see that the \begin{align*}r^2\end{align*}
It is a very good match, so the equation representing our data is, therefore, \begin{align*}y = 15.06(1.274^x)\end{align*}
The last part of our problem asked us to determine what the count was after 7.5 hours. In other words, what is \begin{align*}y\end{align*}
\begin{align*}y & = 15.06 \left (1.274^x \right )\\
y & = 15.06 \left (1.274^{7.5} \right )\\
y & = 15.06 \left (6.149 \right )\\
y & = 92.6 \ \text{atoms}\end{align*}
We can check this on our calculator as follows:
Our calculation is a bit over, because we rounded the values for \begin{align*}a\end{align*}
Example 17
Jack believes that the concentration of gold decreases exponentially as you move further and further away from the main body of ore. He collects the following data to test out his theory:
Distance (m)  Concentration (g/t) 

0  320 
400  80 
800  20 
1,200  5 
1,600  1.25 
2,000  0.32 
Is this data representative of an exponential distribution? If so, find the equation. What is the concentration at 1,000 m?
Solution:
Again, we can plot this data using pencil and paper, or we can use a graphing calculator. As with Example 16, we will use a graphing calculator here.
The resulting graph appears as follows:
At a glance, it does look like an exponential curve, but we really have to take a closer look by doing the exponential regression.
In the analysis of the exponential regression, we see that the \begin{align*}r^2\end{align*}
It is a very good match, so the equation representing our data is, therefore, \begin{align*}y = 318.56 \left (0.9965^x \right )\end{align*}
The problem asks, “What is the concentration at 1,000 m?” This question can be answered as shown below:
\begin{align*}y & = 318.56 \left (0.9965^x \right )\\
y & = 318.56 \left (0.9965^{1000} \right )\\
y & = 318.56(0.03001)\\
y & = 9.56 \ \text{g/t}\end{align*}
Therefore, the concentration of gold is 9.56 grams of gold per ton of rock.
We can check this on our calculator as follows:
Our calculation is a bit under, because we rounded the values for \begin{align*}a\end{align*}
Points to Consider
 Why is a normal distribution considered to be a continuous probability distribution, whereas a binomial distribution is considered to be a discrete probability distribution?
 How can you tell if a curve is truly an exponential distribution curve?
Vocabulary
 Binomial distribution
 A probability distribution of the successful trials of a binomial experiment.
 Binomial experiments
 Experiments that include only 2 choices, with distributions that involve a discrete number of trials of these 2 possible outcomes.

Coefficient of determination \begin{align*}(r^2)\end{align*}
(r2)  A standard quantitative measure of best fit. Has values from 0 to 1, and the closer the value is to 1, the better the fit.
 Continuous data
 Data where an infinite number of values exist between any 2 other values. Data points are joined on a graph.
 Continuous random variable
 A variable that can form an infinite number of groupings.
 Continuous variables
 Variables that take on any value within the limits of the variable.
 Discrete values
 Data where a finite number of values exist between any 2 other values. Data points are not joined on a graph.
 Distribution
 The description of the possible values of a random variable and the possible occurrences of these values.
 Exponential distribution

A probability distribution showing a relation in the form \begin{align*}y = ab^x\end{align*}
y=abx .
 Normal distribution curve
 A symmetrical curve that shows the highest frequency in the center (i.e., at the mean of the values in the distribution) with an identical curve on either side of that center.
 Standard distributions
 Normal distributions, which are often referred to as bell curves.
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