SOCY HW 11 lassek

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2061

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Jan 9, 2024

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HW11 – SOCY 2061. Due Dec 3 rd , 10pm on CanvasNAME: ________________________ Using the data states.RData , please examine infant mortality ( infant ) at the state level as a function of three indicators of socioeconomic status: education ( hsdiploma ), income ( inc ), and minimum wage ( minwage ). That is, run a regression with infant as the dependent variable and the three indicators of SES as independent variables. And answer the following questions.Please put your answers in this document below each question. You can use the ‘snip’ feature to past the output and syntax if you’d like to preserve what it looks like in R. 1. Please provide the script you used for your equation below summary(Im(infant~inc+minwage+hsdiploma, data=states)) 2. Please provide the output for your model below Call: Im(formula = infant ~ inc + minwage + hsdiploma, data = states) residuals: Min TO Median 30мах -2.39978 -0.69423 0.00637 0.67378 3.03006 Coetticients: Estimate Std. Error t value Pr(> [t|) (Intercept) 2.412e+01 3.657e+00 6.596 3.68e-08 inc -7.724-05 3.087e-05 -2.502 0.01598 * minwage -3.336-01 2.201e-01 -1.516 0.13638 hsdiploma -1.342e-01 4.442e-02 -3.021 0.00411 Signif. codes: 0 ***** 0.001 **** 0.01 *** 0.05 " 0.1 ** 1 Residual standard error: 1.136 on 46 degrees of freedom Multiple R-squared: 0.4121, Adjusted R-squared: 0.3737 F-statistic: 10.75 on 3 and 46 DF, p-value: 1.799-05 3. Please provide standardized regression estimates below summary(Im(scale(infant) ~scale(inc) + scale(minwage) + scale(hsdiploma), data = states)) Call: Im(formula = scale(infant) ~ scale(inc) + scale(minwage) + scale(hsdiploma), data= states)

Residuals: Min 1Q Median 3Q Max -1.67194 -0.48368 0.00444 0.46943 2.11106 Coefficients: Estimate Std. Error t value Pr(>|t |) (Intercept) scale(inc) 1.454e-15 1.119-01 0.000 1.00000 -3.138-01 1.254-01 -2.502 0.01598 * scale(minwage) -1.794e-01 1.184e-01 - 1.516 0.13638 scale(hsdiploma) -3.702e-01 1.226e-01 -3.021 0.00411 ** Signif. codes: 0 '***' 0.001 ***' 0.01 '*' 0.05 0.1 ‘1’ Residual standard error: 0.7914 on 46 degrees of freedom Multiple R-squared: 0.4121, Adjusted R-squared: 0.3737 F-statistic: 10.75 on 3 and 46 DF, p-value: 1.799-05 ● 4. Please interpret the values from questions 2 and 3 below. What do the unstandardized and standardized regression estimates tell you about infant mortality and these three indicators of socio-economic status at the state level. 5. Please interpret the meaning of the r-squared for this model 6. Now run the same model but include a control for region like we did before (recall we used the as.factor statement). Please include your syntax below. summary(Im(infant ~ inc + minwage + hsdiploma + as.factor(region), data = states)) 7. Please include your output here. Call: Im(formula = infant ~ inc + minwage + hsdiploma + as.factor(region), data = states) Residuals: min 1Q Median 3Q Max -2.0620 -0.5222 0.0411 0.7217 2.6110 Coefficients: Estimate Std. Error t value Pr(> | t|) (intercept) 1.357e+01 4.204e+00 3.228 0.002388 ** InC -7.829-05 3.119-05 -2.510 0.015899 * Ininwage -2.764e-02 2.031-01 -0.136 0.892353

hsdiploma -2.480e-02 4.966e-02 -0.499 0.620054 as.factor(region)West -2.030e+00 4.762e-01 -4.262 0.000109 *** as.factor(region)Midwest -1.172+00 4.783-01 -2.451 0.018409 * as.factor(region)Northeast -1.459+00 5.347e-01 -2.729 0.009175 ** Signif. codes: 0 '**** 0.001 '*** 0.01 *** 0.05 " 0.1114 Residual standard error: 0.9796 on 43 degrees of freedom Multiple R-squared: 0.5913, Adjusted R-squared: 0.5342 F-statistic: 10.37 on 6 and 43 DF, p-value: 4.348-07 8. Please interpret the results focusing on the effect of region but also changes in the effects of income, minimum wage, and hsdiploma. - The region variable is statistically significant, indicating that there are regional differences in infant mortality rates - Income remains a significant predictor, suggesting that higher income is associated with lower infant mortality. - The effects of minimum wage and high school diploma rates are not statistically significant after including the region variable. This suggests that the regional differences captured by the new variable may account for some of the variability in infant mortality that was previously attributed to these variables. 9. Please evaluate the possibility that income has a non-linear effect of infant mortality by including a quadratic term in your model and note the significance. summary(Im(infant ~ inc + |(inc^2) + minwage + hsdiploma + as.factor(region), data = states)) Call: Im(formula = infant ~ inc + |(inc^2) + minwage + hsdiploma + as.factor(region), data = states) ReSiG Ang Min 1Q Median 3Q Max -2.08108 -0.51688 0.05591 0.70172 2.56848 Coeflicients: Estimate Std. Error t value Pr(>| t|) (Intercept) 1.465e+01 7.301e+00 2.007 0.051238.

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inc -1.374e-04 3.254e-04 -0.422 0.675105 linc^2) 6.878-10 3.771e-09 0.182 0.856155 minware -2.670e-02 2.054e-01 -0.130 0.897231 hsdiploma -2.303e-02 5.115e-02 -0.450 0.654826 as.factor(region)West -2.027e+00 4.819e-01 -4.208 0.000133 *** as.factor(region)Midwest -1.159+00 4.894e-01 -2.367 0.022602 * as.factor(region)Northeast -1.472e+00 5.454e-01 -2.699 0.009981 ** Signif. codes: 0 ***** 0.001 '*** 0.01 *** 0.05 " 0.1 ' 1 Residual standard error: 0.9908 on 42 degrees of freedom Multiple R-squared: 0.5916, Adjusted R-squared: 0.5235 F-statistic: 8.691 on 7 and 42 DF, p-value: 1.471-06 10. Create a plot similar to the one we used in class to examine the possibility of the non-linear relationship between income at the state level and infant mortality. Please describe what your plot shows and how it supports what you found in question 9. ggplot(data = states, aes(x = inc, y = infant)) + geom Doin + geom_smooth(method = "Im", formula = y~ x+|(×^2))

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