The estimated regression equation is of the form:

$\hat{y} = b_0 + b_1 x$

Where:

• $\hat{y}$ is the estimated revenue (in thousands of dollars),
• $x$ is the month,
• $b_0$ is the intercept,
• $b_1$ is the slope of the line (representing the change in revenue per month).

From the regression output:

• The intercept $b_0 = 470.5033$,
• The slope $b_1 = 28.5728$.

Thus, the estimated regression equation is:

$\hat{y} = 470.5033 + 28.5728x$

This equation predicts the company's revenue based on the month.

Would you like more details or have any further questions?

Here are 5 related questions:

1. How do you interpret the slope $b_1$ in the context of this problem?
2. What is the significance of the intercept $b_0$ for this company’s revenue?
3. What does the R Square value tell us about the fit of the model?
4. How would you calculate the predicted revenue for month 20?
5. What is the purpose of the Standard Error in regression analysis?

Tip: The closer the value of $R^2$ is to 1, the better the model explains the variability of the response variable.