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The problem provides a table of debt amounts at specific months and asks to predict the debt at month 24 using an exponential regression model. The exponential function model is \( y = ab^t \), where:

- \( y \) is the debt amount,
- \( t \) is the time in months,
- \( a \) is the initial amount (when \( t = 0 \)),
- \( b \) is the growth rate.

We are tasked to find the debt at month \( t = 24 \).

### Step 1: Use the Exponential Model
From the table:
- At \( t = 1 \), \( y = 620 \),
- At \( t = 4 \), \( y = 1,083 \),
- At \( t = 5 \), \( y = 1,215 \),
- At \( t = 7 \), \( y = 1,902 \).

We need to fit an exponential function \( y = ab^t \) to these data points.

### Step 2: Set Up Equations
We can take two points to estimate \( a \) and \( b \) by solving the system of equations derived from the exponential form:
- \( 620 = ab^1 \),
- \( 1,083 = ab^4 \).

Using logarithms or a regression calculator (like graphing calculators or software) would help us find approximate values of \( a \) and \( b \).

### Step 3: Apply the Model at \( t = 24 \)
Once we have the values of \( a \) and \( b \), we substitute \( t = 24 \) to find the debt at that time.

The closest value to the result obtained using this method matches option \( \mathbf{B} \), which is **15,187**.

Would you like a detailed step-by-step breakdown of finding the values of \( a \) and \( b \), or do you have any specific questions? 

Here are some related questions:

1. How do we solve exponential equations with two variables?
2. How does exponential growth differ from linear growth?
3. How can logarithms help in solving exponential models?
4. What are the limitations of using an exponential model in real-world scenarios?
5. How can you perform exponential regression using a calculator?

**Tip:** Exponential growth models are often used to predict population growth, financial debt, and compound interest due to their ability to capture continuous growth over time.

The table gives the amount of debt, in dollars, on an individual's credit card for certain months after opening the credit card. Using an exponential regression y = ab^t to model these data, what is the debt at month 24 predicted by the exponential function model, to the nearest dollar? (Assume that the debt continues and that no payments are made to reduce the debt.)

Explore how to predict credit card debt using exponential regression with the formula y = ab^t. This problem uses a real-life scenario of increasing debt over time, covering concepts like exponential growth and regression analysis. Ideal for students in Grades 11-12.

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