Dependent Variable: disability1 Independent Variables: (1) Constant, (2) Person's age (ecage26), (3) male1 "male1" is equal to 1 for males and 0 for females; disability1 is equal to 1 for disabled and 0 for not disabled regress disabilityl ecage26 malel Source | SS at MS Model Residual | 1137.25995 9104.04738 47702 2 568.629974 19085253 Total | 10241.3073 47704 .214684457 Number of El 2, 47702) Prob > F R-squared ke = 47705 = 2979.42 = 0.0000 = 0.1110 0.1110 = .43687 Adj R-squared = Root MSE disability1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] ecage26 .0084218 male1 | -.0084567 cons -.0891858 0001093 77.04 0.000 .0040058 -2.11 0.035 .0060068 -14.85 0.000 0082076 .0086361 -.0163082 -.0006052 -.1009592 -.0774125 1.1 Interpret the estimated values (including the constant) in the following two senses: a) What do the estimated coefficients mean mathematically? Use the estimated values in your answer. b) What are some economic reasons why the estimated coefficients might take on the values that they do? 1.2 You can use your estimated coefficients to form a linear function predicting the probability of a disability for either sex at any age. Solve this equation to find the age at which the predicted probability of a disability for a female is equal to 0. Remember that for a female, the variable male1 = 0. For what age range is the predicted probability of a disability < 0 for a female?