Chapter 7 Exponential Functions
We next consider another important family of functions, called exponential functions. These functions describe growth by a constant factor in equal time periods. Exponential functions model many familiar processes, including the growth of populations, compound interest, and radioactive decay. Here is an example.
In 1965, Gordon Moore, the cofounder of Intel, observed that the number of transistors on a computer chip had doubled every year since the integrated circuit was invented. Moore predicted that the pace would slow down a bit, but the number of transistors would continue to double every 2 years. More recently, data density has doubled approximately every 18 months, and this is the current definition of Moore's law. Most experts, including Moore himself, expected Moore's law to hold for at least another two decades.
| Year | Name of circuit | Transistors |
| \(1971\) | \(4004\) | \(2300\) |
| \(1972\) | \(8008\) | \(3300\) |
| \(1974\) | \(8080\) | \(6000\) |
| \(1978\) | \(8086\) | \(29,000\) |
| \(1979\) | \(8088\) | \(30,000\) |
| \(1982\) | \(80286\) | \(134,000\) |
| \(1985\) | \(80386\) | \(275,000\) |
| \(1989\) | \(90486\) | \(1,200,000\) |
| \(1993\) | Pentium | \(3,000,000\) |
| \(1995\) | Pentium Pro | \(5,500,000\) |
| \(1997\) | Pentium II | \(7,500,000\) |
| \(1998\) | Pentium II Xeon | \(7,500,000\) |
| \(1999\) | Pentium III | \(9,500,000\) |
The data shown are modeled by the exponential function \(N(t) = 2200(1.356)^t\text{,}\) where \(t\) is the number of years since \(1970\text{.}\)
Investigation 7.1. Population Growth.
In a laboratory experiment, researchers establish a colony of \(100\) bacteria and monitor its growth. The colony triples in population every day.
| \(~t~\) | \(P(t)\) | ||
| \(~0~\) | \(100\) | \(\blert{P(0)=100}\) | |
| \(~1~\) | \(\) | \(\blert{P(1)=100\cdot 3=}\) | |
| \(~2~\) | \(\) | \(\blert{P(2)=[100\cdot 3]\cdot 3=}\) | |
| \(~3~\) | \(\) | \(\blert{P(3)=}\) | |
| \(~4~\) | \(\) | \(\blert{P(4)=}\) | |
| \(~5~\) | \(\) | \(\blert{P(5)=}\) |
Fill in the table showing the population \(P(t)\) of bacteria \(t\) days later.
Plot the data points from the table and connect them with a smooth curve.
Write a function that gives the population of the colony at any time \(t\text{,}\) in days. Hint: Express the values you calculated in part (1) using powers of \(3\text{.}\) Do you see a connection between the value of \(t\) and the exponent on \(3\text{?}\)
Graph your function from part (3) using a calculator. (Use the table to choose an appropriate window.) The graph should resemble your hand-drawn graph from part (2).
Evaluate your function to find the number of bacteria present after 8 days. How many bacteria are present after 36 hours?
Investigation 7.2. Exponential Decay.
A small coal-mining town has been losing population since 1940, when 5000 people lived there. At each census thereafter (taken at 10-year intervals), the population declined to approximately 0.90 of its earlier figure.
| \(t\) | \(P(t)\) | ||
| \(0\) | \(5000\) | \(\blert{P(0)=5000}\) | |
| \(10\) | \(\) | \(\blert{P(10)=5000\cdot 0.90=}\) | |
| \(20\) | \(\) | \(\blert{P(20)=[5000\cdot 0.90]\cdot 0.90=}\) | |
| \(30\) | \(\) | \(\blert{P(3)=}\) | |
| \(40\) | \(\) | \(\blert{P(4)=}\) | |
| \(50\) | \(\) | \(\blert{P(5)=}\) |
Fill in the table showing the population \(P(t)\) of the town \(t\) years after 1940.
Plot the data points and connect them with a smooth curve.
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Write a function that gives the population of the town at any time \(t\) in years after 1940.
Hint: Express the values you calculated in part (1) using powers of 0.90. Do you see a connection between the value of \(t\) and the exponent on 0.90?
Graph your function from part (3) using a calculator. (Use the table to choose an appropriate window.) The graph should resemble your hand-drawn graph from part (2).
- Evaluate your function to find the population of the town in 1995. What was the population in 2000?
