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The table below shows the population of a town over [tex]$x$[/tex] years.

\begin{tabular}{|c|c|}
\hline
Years & Population \\
\hline
0 & 10,500 \\
\hline
5 & 16,000 \\
\hline
10 & 26,000 \\
\hline
15 & 40,000 \\
\hline
20 & 65,000 \\
\hline
\end{tabular}

What values, rounded to the nearest tenth, complete the exponential regression equation that models the data?

[tex] f(x) = \square \cdot e^{\square x} [/tex]

Based on the regression equation and rounded to the nearest whole person, what is the estimated population after 25 years?

[tex] \square [/tex] people


Sagot :

To solve the problem, we need to find an equation that models the population growth in the town over the given years using an exponential regression equation. The general form of an exponential function is:

[tex]\[ f(x) = a \cdot e^{b \cdot x} \][/tex]

We are given the following data points:

[tex]\[ \begin{array}{|c|c|} \hline \text{Years (x)} & \text{Population (f(x))} \\ \hline 0 & 10,500 \\ \hline 5 & 16,000 \\ \hline 10 & 26,000 \\ \hline 15 & 40,000 \\ \hline 20 & 65,000 \\ \hline \end{array} \][/tex]

Through the process of exponential regression, we can determine the constants [tex]\(a\)[/tex] and [tex]\(b\)[/tex] that best fit this data set.

The result of our calculation gives us the values:
[tex]\[ a \approx 10134.7 \][/tex]
[tex]\[ b \approx 0.1 \][/tex]

Thus, the exponential equation that models the data is:
[tex]\[ f(x) = 10134.7 \cdot e^{0.1 \cdot x} \][/tex]

Next, we need to use this equation to estimate the population after 25 years. We substitute [tex]\( x = 25 \)[/tex] into our equation:

[tex]\[ f(25) = 10134.7 \cdot e^{0.1 \cdot 25} \][/tex]

Calculating this yields an estimated population of approximately 102,905 people.

To conclude:
1. The exponential regression equation that models the data is:
[tex]\[ f(x) = 10134.7 \cdot e^{0.1 \cdot x} \][/tex]

2. The estimated population after 25 years is:
[tex]\[ 102,905 \text{ people} \][/tex]
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