Write the estimated regression equation using the least squares estimates for b0b0 and b1b1. Round to four decimal places, if necessary.

The estimated regression equation based on the least squares estimates can be written in the form:

\[
\text{Revenue} = b_0 + b_1 \times \text{Month}
\]

Given the coefficients from the regression analysis:
- \( b_0 \) (Intercept) = 479.4474 (rounded to four decimal places)
- \( b_1 \) (Slope) = 25.8098 (rounded to four decimal places)

The estimated regression equation is:

\[
\text{Revenue} = 479.4474 + 25.8098 \times \text{Month}
\]

This equation predicts the revenue (in thousands of dollars) based on the month number.

Would you like any further details or explanations on this? Here are some related questions to consider:

1. How do the values of \( b_0 \) and \( b_1 \) influence the predicted revenue?
2. What does the intercept (\( b_0 \)) represent in this context?
3. How would the predicted revenue change if the slope \( b_1 \) were higher or lower?
4. What is the importance of rounding coefficients in the regression equation?
5. How does this regression equation help in forecasting future revenues?
6. Can the regression equation be used for months beyond the data provided (extrapolation)?
7. How might changes in the data impact the coefficients \( b_0 \) and \( b_1 \)?
8. What are potential limitations of using a linear regression model in this scenario?

**Tip:** Always consider the context when interpreting the intercept and slope in a regression equation. The intercept might not always have a meaningful interpretation, especially when extrapolating beyond the observed data range.

Learn how to write the estimated regression equation using least squares estimates. This problem involves applying linear regression concepts to predict revenue based on month data, with detailed coefficients provided. Suitable for advanced high school students and beyond.

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