Alagendram's question via email about approximating change

In summary, the population size of a colony of bacteria can be represented by the equation $\displaystyle \begin{align*} N(t) = N_0\,\mathrm{e}^{\frac{k\,t}{100}} \end{align*}$, and a small change in the population size over a short interval of time can be approximated by $\displaystyle \begin{align*} \frac{\Delta\,N}{N} \approx \frac{k}{100}\,\Delta\,t \end{align*}$. In the space of one hour, the approximate percentage change in the population size is $\displaystyle \begin{align*} \frac{k}{100} \end{align*
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The population of a colony of bacteria of size $\displaystyle \begin{align*} N \end{align*}$ is given by $\displaystyle \begin{align*} N(t) = N_0\,\mathrm{e}^{\frac{k\,t}{100}} \end{align*}$, where $\displaystyle \begin{align*} t \end{align*}$ is the time in hours.

(a) Use calculus to show that a small change $\displaystyle \begin{align*} \Delta\,N \end{align*}$ in the population size in a small interval of time $\displaystyle \begin{align*} \Delta \, t \end{align*}$ satisfies $\displaystyle \begin{align*} \frac{\Delta\,N}{N} \approx \frac{k}{100}\,\Delta\,t \end{align*}$.

(b) In the space of any hour, what is the approximate percentage change in the size of the population?

(a) We should recall that for a small change in x, then $\displaystyle \begin{align*} \frac{\mathrm{d}y}{\mathrm{d}x} \approx \frac{\Delta\,y}{\Delta\,x} \end{align*}$, so $\displaystyle \begin{align*} \Delta\,y \approx \frac{\mathrm{d}y}{\mathrm{d}x}\,\Delta\,x \end{align*}$, so in this case

$\displaystyle \begin{align*} N &= N_0\,\mathrm{e}^{\frac{k\,t}{100}} \\ \frac{\mathrm{d}N}{\mathrm{d}t} &= N_0\,\frac{k}{100}\,\mathrm{e}^{\frac{k\,t}{100}} \\ \\ \Delta\,N &\approx \frac{\mathrm{d}N}{\mathrm{d}t}\,\Delta\,t \\ &= N_0\,\frac{k}{100}\,\mathrm{e}^{\frac{k\,t}{100}}\,\Delta\,t \\ \\ \frac{\Delta\,N}{N} &\approx \frac{N_0\,\frac{k}{100}\,\mathrm{e}^{\frac{k\,t}{100}}\,\Delta\,t}{N} \\ &= \frac{N_0\,\frac{k}{100}\,\mathrm{e}^{\frac{k\,t}{100}}\,\Delta\,t}{N_0\,\mathrm{e}^{\frac{k\,t}{100}}} \\ &= \frac{k}{100}\,\Delta\,t \end{align*}$(b) In the space of one hour, $\displaystyle \begin{align*} \Delta\,t = 1 \end{align*}$, so that means

$\displaystyle \begin{align*} \frac{\Delta\,N}{N} &\approx \frac{k}{100}\,\Delta\,t \\ &= \frac{k}{100}\cdot 1 \\ &= \frac{k}{100} \\ \Delta\,N &\approx \frac{k}{100}\,N \end{align*}$

So the change in N is approximately k% in the space of one hour.
 
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  • #2
This means that the population of bacteria is increasing exponentially, with the percentage of increase being directly proportional to the growth rate k.
 

FAQ: Alagendram's question via email about approximating change

What is the purpose of approximating change?

Approximating change is a mathematical technique used to estimate the rate of change of a function at a given point. It allows us to make predictions or understand the behavior of a system without having to rely on precise data points.

How is approximating change different from finding the exact change?

Finding the exact change involves using calculus to find the derivative of a function at a specific point. Approximating change, on the other hand, uses simpler mathematical techniques such as linear or quadratic approximations to estimate the rate of change.

What type of functions can be used with approximating change?

Approximating change can be used with any continuous function, meaning a function with no breaks or jumps. This includes polynomial, exponential, logarithmic, and trigonometric functions.

How accurate is approximating change compared to finding the exact change?

The accuracy of approximating change depends on the method used and the complexity of the function. Generally, the closer the approximation point is to the desired point, the more accurate the approximation will be. However, it will never be as precise as finding the exact change using calculus.

When is approximating change useful in real-world applications?

Approximating change is useful in situations where we need to make predictions or understand the behavior of a system without having access to precise data. It is commonly used in economics, physics, and engineering to model and analyze real-world phenomena.

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