Exponential Regression

The table below shows the population of a fictional California Gold Rush Town named Lehi in the years after its peak population in 1880.
\begin{tabular}{|c|l|l|l|l|l|l|}
\hline Year & 1880 & 1890 & 1900 & 1910 & 1920 & 1930 \\
\hline Population & 9800 & 5081 & 4331 & 3542 & 1914 & 1081 \\
\hline
\end{tabular}

For the purpose of this problem, let [tex]$P$[/tex] represent the population of Lehi [tex]$t$[/tex] years after 1880 ([tex]$t=0$[/tex] represents 1880). The new table is:
\begin{tabular}{|c|r|r|r|r|r|r|}
\hline[tex]$t$[/tex] & 0 & 10 & 20 & 30 & 40 & 50 \\
\hline[tex]$P(t)$[/tex] & 9800 & 5081 & 4331 & 3542 & 1914 & 1081 \\
\hline
\end{tabular}

1. Use your calculator to determine the exponential regression equation that models the set of data above. Round the [tex]$a$[/tex] value to two decimals, and round the [tex]$b$[/tex] value to two decimals. Use the indicated variables and proper function notation.
[tex]\[ P(t) = \][/tex]
[tex]\[\square\][/tex]

2. Based on your regression model, what is the percent decrease per year?
[tex]\[\square \%\][/tex]

3. Find [tex]$P(35)$[/tex]. Round your answer to the nearest whole number.
[tex]\[ P(35) = \][/tex]
[tex]\[\square\][/tex]

4. Interpret your answer by completing the following sentence. Be sure to use units in your answer.
"The population of Lehi [tex]$\square$[/tex] after 1880 was about [tex]$\square$[/tex]."

5. How long did it take for the population of Lehi to reach 350 people? Round your answer to the nearest whole number.
[tex]\[ P(t) = 350 \text{ when } t = \][/tex]
[tex]\[\square\][/tex]



Answer :

To determine the exponential regression equation that best models the given data, we follow these steps:

1. Data Preparation:
Given data for the years after 1880:

| [tex]\(t\)[/tex] (years after 1880) | 0 | 10 | 20 | 30 | 40 | 50 |
|---------------------------|---|----|----|----|----|----|
| [tex]\(P(t)\)[/tex] (population) | 9800 | 5081 | 4331 | 3542 | 1914 | 1081 |

2. Exponential Model:
The form of the exponential regression model we are fitting is
[tex]\[ P(t) = a \cdot e^{bt} \][/tex]

3. Parameters:
After computing the best fit using exponential regression, we obtain:
[tex]\[ a \approx 0.00 \quad \text{and} \quad b \approx 1.00 \][/tex]
Thus, the exponential regression model is:
[tex]\[ P(t) = 0.00 \cdot e^{1.00t} \][/tex]

4. Percent Decrease Per Year:
To find the percent decrease per year, we use the formula:
[tex]\[ \text{Percent Decrease Per Year} = (1 - e^b) \times (-100) \% \][/tex]
Plugging in the value of [tex]\( b \)[/tex]:
[tex]\[ \text{Percent Decrease Per Year} \approx 171.83 \% \][/tex]

5. Population at [tex]\( t = 35 \)[/tex]:
Using the regression model to find the population at [tex]\( t = 35 \)[/tex]:
[tex]\[ P(35) = 0.00 \cdot e^{1.00 \times 35} = 0 \][/tex]
Rounding to the nearest whole number:
[tex]\[ P(35) \approx 0 \][/tex]

6. Interpretation of [tex]\( P(35) \)[/tex]:
"The population of Lehi 35 years after 1880 was about 0."

7. Time to Reach Population of 350:
To find the time [tex]\( t \)[/tex] when the population [tex]\( P(t) = 350 \)[/tex]:
[tex]\[ 350 = a \cdot e^{bt} \][/tex]
Solving for [tex]\( t \)[/tex]:
[tex]\[ t = \frac{\ln(350/a)}{b} \][/tex]
Using [tex]\( a \approx 0.00 \)[/tex] and [tex]\( b \approx 1.00 \)[/tex]:
[tex]\[ t \approx 49 \][/tex]
So it takes approximately 49 years for the population to reach 350 people.

In summary:

1. The exponential regression model is:
[tex]\[ P(t) = 0.00 \cdot e^{1.00t} \][/tex]

2. The percent decrease per year is approximately:
[tex]\[ 171.83 \% \][/tex]

3. The population at [tex]\( t = 35 \)[/tex] years is:
[tex]\[ P(35) = 0 \][/tex]

4. It takes approximately:
[tex]\[ P(t) = 350 \text{ when } t = 49 \][/tex] years for the population to reach 350 people.