reg log_premature_death 1n_income Source | SS df 1 Model I 133.017756 Residual | 122.732526 3,036 -----+-...

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reg log_premature_death 1n_income Source | SS df 1 Model I 133.017756 Residual | 122.732526 3,036 -----+- 255.750282 3,037 Coef. Std. Err. MS 3,038 3290.42 133.017756 0.0000 .040425733 0.5201 0.5199 .084211486 .20106 t P>|t| [95% Conf. Interval] 1n_income | -.8685173 .0151409 -57.36 0.000 -.8982048 -.8388298 17.94798 cons | 18.26667 .1625329 112.39 0.000 18.58535 Ln_income median income in the community in the LOG form log_premature_death Years of potential life lost before age 75 per 100,000 population in the LOG form What is the correct interpretation of the "effect" of income on premature death? If income goes up by $1, premature deaths go down by .86 of a year of a potential life lost. If income goes up by $1, premature deaths go down by .86%. premature deaths go down by .86 of a year of a potential life lost. If income goes up by 1%, If income goes up by 1%, If income goes up by 1%, premature deaths go down by .86%. premature deaths go down by 86%. Total I log_premat-h | Number of obs F(1, 3036) Prob > F R-squared Adj R-squared Root MSE = reg log_premature_death 1n_income Source | SS df 1 Model I 133.017756 Residual | 122.732526 3,036 -----+- 255.750282 3,037 Coef. Std. Err. MS 3,038 3290.42 133.017756 0.0000 .040425733 0.5201 0.5199 .084211486 .20106 t P>|t| [95% Conf. Interval] 1n_income | -.8685173 .0151409 -57.36 0.000 -.8982048 -.8388298 17.94798 cons | 18.26667 .1625329 112.39 0.000 18.58535 Ln_income median income in the community in the LOG form log_premature_death Years of potential life lost before age 75 per 100,000 population in the LOG form What is the correct interpretation of the "effect" of income on premature death? If income goes up by $1, premature deaths go down by .86 of a year of a potential life lost. If income goes up by $1, premature deaths go down by .86%. premature deaths go down by .86 of a year of a potential life lost. If income goes up by 1%, If income goes up by 1%, If income goes up by 1%, premature deaths go down by .86%. premature deaths go down by 86%. Total I log_premat-h | Number of obs F(1, 3036) Prob > F R-squared Adj R-squared Root MSE =

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ANSWER This is a log model the coefficient on income pro... View the full answer