2. We fit a model for pizza expenditure with two explanatory variables female and age: pizza = 1 + 2female + Bage + u. Using N = 36 observations, we have the following regression output: regress pizza female age Source | SS df MS Number of obs 3 F( 2, 33) = 8.72 Model Residual 271279.331 513299.669 2 135639.666 33 15554.5354 Prob > F = 0.0009 R-squared = 0.345{ Adj R-squared = 0.306: Total | 784579 35 22416.5429 Root MSE = 124.72 pizza | Coef. Std. Err. t P>Itl [95% Conf. Interval_ female-170.3778 42.15349 _cons | age -3.674645 2.202123 413.43 80.36956 -4.04 0.000 -1.67 0.105 5.14 0.000 -256.1397 -84.6158 -8.154897 .805607( 249.9169 576.943: Now, we create a new dummy variable male such that male = 1 - female, and fit a new regression pizza =Y1Y2male + Yage + u. Write down the fitted regression for this new regression (2pt), and interpret the intercept and slope coefficients (2pt).