To find the demand forecast for Quarter 2 of the next year, we will use the given regression equation:

$\hat{y_t} = 3,305 - 665Qtr1 - 1,335Qtr2 + 305Qtr3$

For Quarter 2:

• $Qtr1 = 0$
• $Qtr2 = 1$
• $Qtr3 = 0$

Substituting these values into the equation:

$\hat{y_t} = 3,305 - 665(0) - 1,335(1) + 305(0)$

Simplifying this:

$\hat{y_t} = 3,305 - 1,335$

$\hat{y_t} = 1,970$

So, the demand forecast for Quarter 2 of the next year is $\boxed{1,970}$.

Would you like more details or have any questions about this problem? Here are 8 additional questions related to this topic:

1. How do dummy variables help in regression analysis?
2. What is the interpretation of the coefficient -665 for $Qtr1$ in the regression equation?
3. Why is it important to check the statistical significance of the regression coefficients?
4. How would you interpret a positive coefficient for $Qtr3$ in the context of this regression equation?
5. How can seasonal variations be accounted for in regression models?
6. What are some potential limitations of using a regression model for forecasting?
7. How would the forecast change if the regression equation included interaction terms between quarters?
8. Can this regression model be used for long-term forecasting? Why or why not?

Tip: Always verify the assumptions of your regression model, such as linearity, independence, homoscedasticity, and normality of residuals, to ensure the validity of your forecasts.