2. The model excludes a dummy variable for year 1987. Why has the year dummy for 1987 been excluded? How do you interpret theregression result for the coefficient of the year dummy for 1990 using the Stata output in question 1?3. Using the Stata output in question 1 and the below Stata output to answer this question. Can we reject at the 5% significance level thenull hypothesis that neither per capita income nor population has effect on infant mortality rate (₁ = 0 and ß₂ = 0).reg infmort 1physic d90SourceModelResidualTotalinfmort1physicd90_consSS47.5816152380.920434428.502049df299101Coef. Std. Err.1.928748 .7001339-.9190813 .3892766.0220238 3.680659MS23.79080763.847681154.24259454Number of obsF(2, 99)Prob > FR-squaredAdj R-squaredRoot MSEt P>|t|2.75 0.007-2.36 0.0200.01 0.995=.5395308-1.691491-7.281203====1026.180.00290.11100.09311.9616[95% Conf. Interval]3.317966.14667217.325254. 25 states introduced a free pre-pregnancy and prenatal care program in 1988, while the rest did not have such a program. Withoutcontrolling for any other factors, propose a method that allows you to investigate the effect of the free pre-pregnancy and prenatal careprogram on infant mortality rate. Interpret coefficients in your model.5. Suppose that states in the South tend to have more infant mortality cases because they have more days of extreme heat. At the sametime states in the South tend to be poorer. Explain why this would generate biased estimates in MODEL 1 and propose how to modify thismodel. Notice that you do not observe if a state is located in the South. Part D.Data from the Statistical Abstract of the United States provides a panel data collected at the state level in 1987 and 1990. These data areused to estimate MODEL 1:The variables used in the analysis are:infmort is number of deaths within the year per 1,000 live birthsIpcinc is natural log of per capita incomeIpopul is natural log of the population (the population is in thousands)Iphysic is natural log of physicians per 100,000 inhabitantsd90 is year dummy for 1990.For questions 1 to 4 you can assume that MLR 1-4 are satisfied.A1. Use the Stata output below to interpret ß3. Test at a 5% significance level whether the number of physicians per capita has any effect oninfant mortality rate.reg infmort 1pcinc 1popul 1physic d90SourceModelResidualTotalinfmortinfmort = Po + B₁lpcinc + B₂lpopul + ß3lphysic + 8₁ d90 + uSS78.0499129350.452136428.502049df.19.5124782497 3.61290862Coef. Std. Err.MS101 4.242594541pcinc -4.693354 1.6381321popul- .055142618858841physic3.954843.9733102d90-.090968 .475486334.82137 12.55572_consNumber of obsF(4, 97)Prob > FR-squaredAdj R-squaredRoot MSEt P>|t|-2.87 0.005-0.29 0.7714.06 0.000-0.19 0.8492.770.007=-7.944593-.42943832.023092-1.0346779.901751=1025.400.00060.18210.14841.9008[95% Conf. Interval]-1.442115.31915315.886595.852740759.741

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Data from the Statistical Abstract of the United States provides a panel data collected at the state level in 1987 and 1990. These data are used to estimate MODEL 1: The variables used in the analysis are: infmort is number of deaths within the year per 1,000 live births Ipcinc is natural log of per capita income Ipopul is natural log of the population (the population is in thousands) Iphysic is natural log of physicians per 100,000 inhabitants d90 is year dummy for 1990. For questions 1 to 4 you can assume that MLR 1-4 are satisfied. 1. Use the Stata output below to interpret 3. Test at a 5% significance level whether the number of physicians per capita has any effect on infant mortality rate. reg infmort 1pcinc 1popul 1physic d90 . Source Model Residual Total infmort SS 78.0499129 350.452136 infmort = Bo + B₁lpcinc + ß₂lpopul+ ß3lphysic+8₁d90+ u 428.502049 1pcinc -4.693354 1popul -.0551426 1physic d90 _cons df 4 97 Coef. Std. Err. MS 101 4.24259454 1.638132 .1885884 .9733102 3.954843 -.090968 .4754863 34.82137 12.55572 19.5124782 3.61290862 Number of obs F(4, 97) Prob > F R-squared Adj R-squared Root MSE t P>|t| -2.87 0.005 -0.29 0.771 4.06 0.000 -0.19 0.849 2.77 0.007 = -7.944593 -.4294383 2.023092 -1.034677 9.901751 = = 102 5.40 0.0006 0.1821 0.1484 1.9008 [95% Conf. Interval] -1.442115 .3191531 5.886595 .8527407 59.741

1st Edition

ISBN:9781938168383

Author:Jay Abramson

Publisher:Jay Abramson

Chapter6: Exponential And Logarithmic Functions

Section6.8: Fitting Exponential Models To Data

Problem 3TI: Table 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to...