When rabbits were introduced to the continent of Australia, they quickly multiplied and spread across the continent since there were only primitive marsupial competitors and predators to interfere with the exponential growth of their population. The data in the following table can be used to create a model of rabbit population growth.

\begin{tabular}{|l|c|c|c|c|c|}
\hline
Time (months) & 0 & 3 & 6 & 9 & 12 \\
\hline
No. of Rabbits & 6 & 32 & 107 & 309 & 770 \\
\hline
\end{tabular}

1. Find the regression equation for the rabbit population as a function of time [tex]$x$[/tex].
2. Write the regression equation in terms of base [tex]$e$[/tex].
3. Use the equation from part 2 to estimate the time for the rabbits to exceed 10,000.

A.
1. [tex]$y = 7.898 \times (1.491)^x$[/tex]
2. [tex]$y = 7.898 e^{0.3992 x}$[/tex]
3. [tex]$x = 17.9$[/tex] months

B.
1. [tex]$y = 7.982 \times (1.497)^x$[/tex]
2. [tex]$y = 7.982 e^{0.4033 x}$[/tex]
3. [tex]$x = 17.7$[/tex] months

C.
1. [tex]$y = 7.982 \times (1.907)^x$[/tex]



Answer :

Let's carefully address each part of the question using the data provided, starting with the calculations to find the regression equation.

### 1. Regression Equation in Terms of a Base Other Than [tex]\( e \)[/tex]
Given the data:

| Time (months) | 0 | 3 | 6 | 9 | 12 |
|---------------|---|----|-----|-----|-----|
| No. of Rabbits| 6 | 32 | 107 | 309 | 770 |

We need to determine an exponential growth model of the form [tex]\( y = a \cdot b^x \)[/tex].

Upon performing the necessary regression analysis:
- We find that [tex]\( a \approx 7.898 \)[/tex].
- We also find that the growth rate [tex]\( b \approx 1.491 \)[/tex].

Hence, the regression equation is:
[tex]\[ y = 7.898 \times (1.491)^x \][/tex]

### 2. Regression Equation in Terms of Base [tex]\( e \)[/tex]
In exponential growth models, converting the base from some value [tex]\( b \)[/tex] to the natural base [tex]\( e \)[/tex] involves expressing the equation [tex]\( y = a \cdot b^x \)[/tex] in the form [tex]\( y = a \cdot e^{kx} \)[/tex].

Interestingly, we find:
- [tex]\( a \approx 7.898 \)[/tex]
- [tex]\( k \approx 0.3992 \)[/tex]

Therefore, the regression equation in terms of base [tex]\( e \)[/tex] is:
[tex]\[ y = 7.898 \times e^{0.3992x} \][/tex]

### 3. Estimating the Time for the Population to Exceed 10,000 Rabbits
From the model [tex]\( y = 7.898 \times e^{0.3992x} \)[/tex], we can solve for [tex]\( x \)[/tex] when [tex]\( y = 10,000 \)[/tex].

We need to solve for [tex]\( x \)[/tex] in:
[tex]\[ 10000 = 7.898 \times e^{0.3992x} \][/tex]

Rearranging the equation:
[tex]\[ \frac{10000}{7.898} = e^{0.3992x} \][/tex]

Taking the natural logarithm of both sides:
[tex]\[ \ln\left(\frac{10000}{7.898}\right) = 0.3992x \][/tex]

Thus:
[tex]\[ x = \frac{\ln(10000 / 7.898)}{0.3992} \][/tex]

Upon solving this, we determine that:
[tex]\[ x \approx 17.9 \text{ months} \][/tex]

### Summary
- The regression equation in terms of a base other than [tex]\( e \)[/tex] is:
[tex]\[ y = 7.898 \times (1.491)^x \][/tex]

- The regression equation in terms of base [tex]\( e \)[/tex] is:
[tex]\[ y = 7.898 \times e^{0.3992x} \][/tex]

- The estimated time for the rabbit population to exceed 10,000 is:
[tex]\[ x \approx 17.9 \text{ months} \][/tex]

These results correspond to the first set of answers provided in your question.