Homework Assignement 8
Selene M. Gomez
03 April, 2022
Missing Data Homework
I will use the “voted” outcome variable and the predictor variables will be gender, state the voter lives in, metropolitan areas vs. rural areas, age, nativity, citizenship, longevity of current residence, education and ethnicity. I will then look at the 2020 election results specifically. The only predictor variable that has no missing data was “sex”. the rest of the predictor variables have missing data. The variable with the most missing data id education with about half missing and even though this means I shouldn’t use this variable, I want to include it just to see how this works with variables with only a small amount of data missing and variables with a lot of missing values. I anticipate education variable being the one of the variables with the most significant instability.
Mean imputations were done for age and there were no changed in the averages. Modal imputations were done for the rest of the variables. In the sex variable there was no change since there were no missing cases. In the “metro” variable tehre was only a 1% change for those who live in a metropolitan area. The “voteres” has significant percent changes in all categories with the greatest being 14%. The biggest percent differences betwen the actual data and the imputated data was with the “educ” varaiable with huge differences as big as 30%. After performing regression analysis using the imputed data and comparing it with the actual data, you see notable differences like in education. I think the modal imputations should be an imputation process where it replaces missing data with all possible categories instead of the one most often seen to preserve the percentage ratio of actual data. I would think that would also
## Use of data from IPUMS CPS is subject to conditions including that users should
## cite the data appropriately. Use command `ipums_conditions()` for more details.##
## Please cite as:## Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.## R package version 5.2.2. https://CRAN.R-project.org/package=stargazer## Installing package into 'C:/Users/gomez/OneDrive/Documents/R/win-library/4.1'
## (as 'lib' is unspecified)## package 'factoextra' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\gomez\AppData\Local\Temp\RtmpSunJgt\downloaded_packages## Warning: package 'factoextra' was built under R version 4.1.3## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa## Warning: package 'FactoMineR' was built under R version 4.1.3## sex metro statefip age
## female:57490 inmetro :46146 6 : 9574 80 : 2560
## male :54547 notmetro :21019 48 : 6611 85 : 2319
## outsidemetro:43739 12 : 5055 60 : 1644
## NA's : 1133 36 : 4180 61 : 1588
## 17 : 3115 55 : 1552
## 42 : 2867 (Other):78298
## (Other):80635 NA's :24076
## voteres citizen nativity educ
## 0underayr: 7880 nativeborn :99470 foreignborn:13932 HS :26147
## 1to2yrs : 9384 naturalized: 6326 USborn :97981 BA :18919
## 3to4yrs : 9786 NA's : 6241 NA's : 124 asso/voc/occ: 8945
## 5+yrs :42636 MA : 8075
## NA's :42351 upto8thgrade: 2848
## (Other) : 3007
## NA's :44096
## raceethnicity
## White.Not Latinx :74282
## Latinx :16163
## Black.Not Latinx :11786
## Asian.Not Latinx : 6100
## multiple.Not Latinx: 1391
## (Other) : 1557
## NA's : 758cps20$age <- as.numeric(cps20$age)
summary(cps20$age)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.00 17.00 33.00 32.43 47.00 64.00 24076#mean imputations
cps20$age.imp.mean <- ifelse(is.na(cps20$age)==T, mean(cps20$age, na.rm=T), cps20$age)
mean(cps20$age, na.rm=T)## [1] 32.42821table(cps20$sex)##
## female male
## 57490 54547table(cps20$metro)##
## inmetro notmetro outsidemetro
## 46146 21019 43739table(cps20$statefip)##
## 1 2 4 5 6 8 9 10 11 12 13 15 16 17 18 19
## 2406 1289 1969 1988 9574 1365 1157 1285 1622 5055 2599 1555 2009 3115 1839 1278
## 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
## 1423 1449 2646 869 1385 2285 2597 1416 2121 1662 1996 1446 1561 1472 2109 1737
## 36 37 38 39 40 41 42 44 45 46 47 48 49 50 51 53
## 4180 2608 1745 2797 1646 1846 2867 1001 1973 1314 2278 6611 1936 1436 2128 2073
## 54 55 56
## 2092 1575 1652table(cps20$voteres)##
## 0underayr 1to2yrs 3to4yrs 5+yrs
## 7880 9384 9786 42636table(cps20$citizen)##
## nativeborn naturalized
## 99470 6326table(cps20$nativity)##
## foreignborn USborn
## 13932 97981table(cps20$educ)##
## asso/voc/occ BA HS MA PhD someHS
## 8945 18919 26147 8075 1657 1350
## upto8thgrade
## 2848table(cps20$raceethnicity)##
## AmInAE.Not Latinx Asian.Not Latinx Black.Not Latinx Latinx
## 1165 6100 11786 16163
## multiple.Not Latinx NHPI.Not Latinx White.Not Latinx
## 1391 392 74282#modal imputations
mcv.sex <- factor(names(which.max(table(cps20$sex))), levels=levels(cps20$sex))
mcv.sex## [1] female
## Levels: female malecps20$sex.imp <- as.factor(ifelse(is.na(cps20$sex)==T, mcv.sex, cps20$sex))
levels(cps20$sex.imp) <- levels(cps20$sex)
prop.table(table(cps20$sex))##
## female male
## 0.5131341 0.4868659prop.table(table(cps20$sex.imp))##
## female male
## 0.5131341 0.4868659mcv.metro <- factor(names(which.max(table(cps20$metro))), levels=levels(cps20$metro))
mcv.metro## [1] inmetro
## Levels: inmetro notmetro outsidemetrocps20$metro.imp <- as.factor(ifelse(is.na(cps20$metro)==T, mcv.sex, cps20$metro))
levels(cps20$metro.imp) <- levels(cps20$metro)
prop.table(table(cps20$metro))##
## inmetro notmetro outsidemetro
## 0.4160896 0.1895243 0.3943861prop.table(table(cps20$metro.imp))##
## inmetro notmetro outsidemetro
## 0.4219945 0.1876077 0.3903978mcv.statefip <- factor(names(which.max(table(cps20$statefip))), levels=levels(cps20$statefip))
mcv.statefip## [1] 6
## 51 Levels: 1 2 4 5 6 8 9 10 11 12 13 15 16 17 18 19 20 21 22 23 24 25 26 ... 56cps20$statefip.imp <- as.factor(ifelse(is.na(cps20$statefip)==T, mcv.statefip, cps20$statefip))
levels(cps20$statefip.imp) <- levels(cps20$statefip)
prop.table(table(cps20$statefip))##
## 1 2 4 5 6 8
## 0.021475048 0.011505128 0.017574551 0.017744138 0.085453913 0.012183475
## 9 10 11 12 13 15
## 0.010326946 0.011469425 0.014477360 0.045119023 0.023197694 0.013879343
## 16 17 18 19 20 21
## 0.017931576 0.027803315 0.016414220 0.011406946 0.012701161 0.012933227
## 22 23 24 25 26 27
## 0.023617198 0.007756366 0.012361988 0.020395048 0.023179842 0.012638682
## 28 29 30 31 32 33
## 0.018931246 0.014834385 0.017815543 0.012906451 0.013932897 0.013138517
## 34 35 36 37 38 39
## 0.018824138 0.015503807 0.037309103 0.023278024 0.015575212 0.024964967
## 40 41 42 44 45 46
## 0.014691575 0.016476700 0.025589761 0.008934548 0.017610254 0.011728268
## 47 48 49 50 51 53
## 0.020332569 0.059007292 0.017280006 0.012817194 0.018993725 0.018502816
## 54 55 56
## 0.018672403 0.014057856 0.014745129prop.table(table(cps20$statefip.imp))##
## 1 2 4 5 6 8
## 0.021475048 0.011505128 0.017574551 0.017744138 0.085453913 0.012183475
## 9 10 11 12 13 15
## 0.010326946 0.011469425 0.014477360 0.045119023 0.023197694 0.013879343
## 16 17 18 19 20 21
## 0.017931576 0.027803315 0.016414220 0.011406946 0.012701161 0.012933227
## 22 23 24 25 26 27
## 0.023617198 0.007756366 0.012361988 0.020395048 0.023179842 0.012638682
## 28 29 30 31 32 33
## 0.018931246 0.014834385 0.017815543 0.012906451 0.013932897 0.013138517
## 34 35 36 37 38 39
## 0.018824138 0.015503807 0.037309103 0.023278024 0.015575212 0.024964967
## 40 41 42 44 45 46
## 0.014691575 0.016476700 0.025589761 0.008934548 0.017610254 0.011728268
## 47 48 49 50 51 53
## 0.020332569 0.059007292 0.017280006 0.012817194 0.018993725 0.018502816
## 54 55 56
## 0.018672403 0.014057856 0.014745129mcv.voteres <- factor(names(which.max(table(cps20$voteres))), levels=levels(cps20$voteres))
mcv.voteres## [1] 5+yrs
## Levels: 0underayr 1to2yrs 3to4yrs 5+yrscps20$voteres.imp <- as.factor(ifelse(is.na(cps20$voteres)==T, mcv.voteres, cps20$voteres))
levels(cps20$voteres.imp) <- levels(cps20$voteres)
prop.table(table(cps20$voteres))##
## 0underayr 1to2yrs 3to4yrs 5+yrs
## 0.1130787 0.1346612 0.1404299 0.6118302prop.table(table(cps20$voteres.imp))##
## 0underayr 1to2yrs 3to4yrs 5+yrs
## 0.07033391 0.08375804 0.08734614 0.75856190mcv.citizen <- factor(names(which.max(table(cps20$citizen))), levels=levels(cps20$citizen))
mcv.citizen## [1] nativeborn
## Levels: nativeborn naturalizedcps20$citizen.imp <- as.factor(ifelse(is.na(cps20$citizen)==T, mcv.citizen, cps20$citizen))
levels(cps20$citizen.imp) <- levels(cps20$citizen)
prop.table(table(cps20$citizen))##
## nativeborn naturalized
## 0.94020568 0.05979432prop.table(table(cps20$citizen.imp))##
## nativeborn naturalized
## 0.94353651 0.05646349mcv.nativity <- factor(names(which.max(table(cps20$nativity))), levels=levels(cps20$nativity))
mcv.nativity## [1] USborn
## Levels: foreignborn USborncps20$nativity.imp <- as.factor(ifelse(is.na(cps20$nativity)==T, mcv.nativity, cps20$nativity))
levels(cps20$nativity.imp) <- levels(cps20$nativity)
prop.table(table(cps20$nativity))##
## foreignborn USborn
## 0.1244896 0.8755104prop.table(table(cps20$nativity.imp))##
## foreignborn USborn
## 0.1243518 0.8756482mcv.educ <- factor(names(which.max(table(cps20$educ))), levels=levels(cps20$educ))
mcv.educ## [1] HS
## Levels: asso/voc/occ BA HS MA PhD someHS upto8thgradecps20$educ.imp <- as.factor(ifelse(is.na(cps20$educ)==T, mcv.educ, cps20$educ))
levels(cps20$educ.imp) <- levels(cps20$educ)
prop.table(table(cps20$educ))##
## asso/voc/occ BA HS MA PhD someHS
## 0.13165835 0.27846220 0.38484862 0.11885312 0.02438881 0.01987018
## upto8thgrade
## 0.04191872prop.table(table(cps20$educ.imp))##
## asso/voc/occ BA HS MA PhD someHS
## 0.07983970 0.16886386 0.62696252 0.07207440 0.01478976 0.01204959
## upto8thgrade
## 0.02542017mcv.raceethnicity <- factor(names(which.max(table(cps20$raceethnicity))), levels=levels(cps20$raceethnicity))
mcv.raceethnicity## [1] White.Not Latinx
## 7 Levels: AmInAE.Not Latinx Asian.Not Latinx Black.Not Latinx ... White.Not Latinxcps20$raceethnicity.imp <- as.factor(ifelse(is.na(cps20$raceethnicity)==T, mcv.raceethnicity, cps20$raceethnicity))
levels(cps20$raceethnicity.imp) <- levels(cps20$raceethnicity)
prop.table(table(cps20$raceethnicity))##
## AmInAE.Not Latinx Asian.Not Latinx Black.Not Latinx Latinx
## 0.010469181 0.054817171 0.105913964 0.145247531
## multiple.Not Latinx NHPI.Not Latinx White.Not Latinx
## 0.012500112 0.003522677 0.667529363prop.table(table(cps20$raceethnicity.imp))##
## AmInAE.Not Latinx Asian.Not Latinx Black.Not Latinx Latinx
## 0.010398351 0.054446299 0.105197390 0.144264841
## multiple.Not Latinx NHPI.Not Latinx White.Not Latinx
## 0.012415541 0.003498844 0.669778734library(Amelia)## Loading required package: Rcpp## ##
## ## Amelia II: Multiple Imputation
## ## (Version 1.8.0, built: 2021-05-26)
## ## Copyright (C) 2005-2022 James Honaker, Gary King and Matthew Blackwell
## ## Refer to http://gking.harvard.edu/amelia/ for more information
## ##library(mice)##
## Attaching package: 'mice'## The following object is masked from 'package:stats':
##
## filter## The following objects are masked from 'package:base':
##
## cbind, rbindmd.pattern(cps20[, c("sex", "metro", "age", "statefip","voteres", "citizen", "nativity", "educ", "raceethnicity")])
## sex statefip nativity raceethnicity metro citizen age voteres educ
## 52287 1 1 1 1 1 1 1 1 1
## 16328 1 1 1 1 1 1 1 1 0
## 9422 1 1 1 1 1 1 1 0 1
## 2987 1 1 1 1 1 1 1 0 0
## 700 1 1 1 1 1 1 0 0 1
## 22137 1 1 1 1 1 1 0 0 0
## 4486 1 1 1 1 1 0 1 0 1
## 1145 1 1 1 1 1 0 1 0 0
## 32 1 1 1 1 1 0 0 0 1
## 506 1 1 1 1 1 0 0 0 0
## 576 1 1 1 1 0 1 1 1 1
## 167 1 1 1 1 0 1 1 1 0
## 58 1 1 1 1 0 1 1 0 1
## 19 1 1 1 1 0 1 1 0 0
## 9 1 1 1 1 0 1 0 0 1
## 270 1 1 1 1 0 1 0 0 0
## 17 1 1 1 1 0 0 1 0 1
## 8 1 1 1 1 0 0 1 0 0
## 2 1 1 1 1 0 0 0 0 0
## 199 1 1 1 0 1 1 1 1 1
## 81 1 1 1 0 1 1 1 1 0
## 35 1 1 1 0 1 1 1 0 1
## 15 1 1 1 0 1 1 1 0 0
## 7 1 1 1 0 1 1 0 0 1
## 383 1 1 1 0 1 1 0 0 0
## 24 1 1 1 0 1 0 1 0 1
## 2 1 1 1 0 1 0 1 0 0
## 4 1 1 1 0 1 0 0 0 0
## 1 1 1 1 0 0 1 1 1 1
## 1 1 1 1 0 0 1 1 1 0
## 5 1 1 1 0 0 1 0 0 0
## 37 1 1 0 1 1 1 1 1 1
## 9 1 1 0 1 1 1 1 1 0
## 39 1 1 0 1 1 1 1 0 1
## 5 1 1 0 1 1 1 1 0 0
## 2 1 1 0 1 1 1 0 0 1
## 16 1 1 0 1 1 1 0 0 0
## 9 1 1 0 1 1 0 1 0 1
## 3 1 1 0 1 1 0 1 0 0
## 3 1 1 0 1 1 0 0 0 0
## 1 1 1 0 0 1 1 1 0 1
## 0 0 124 758 1133 6241 24076 42351 44096
##
## 52287 0
## 16328 1
## 9422 1
## 2987 2
## 700 2
## 22137 3
## 4486 2
## 1145 3
## 32 3
## 506 4
## 576 1
## 167 2
## 58 2
## 19 3
## 9 3
## 270 4
## 17 3
## 8 4
## 2 5
## 199 1
## 81 2
## 35 2
## 15 3
## 7 3
## 383 4
## 24 3
## 2 4
## 4 5
## 1 2
## 1 3
## 5 5
## 37 1
## 9 2
## 39 2
## 5 3
## 2 3
## 16 4
## 9 3
## 3 4
## 3 5
## 1 3
## 118779data2 <- cps20
imp <- mice(data = data2[, c("sex", "metro", "age", "statefip","voteres", "citizen", "nativity", "educ", "raceethnicity")], seed=14, m=10)##
## iter imp variable
## 1 1 metro age voteres citizen nativity educ raceethnicity
## 1 2 metro age voteres citizen nativity educ raceethnicity
## 1 3 metro age voteres citizen nativity educ raceethnicity
## 1 4 metro age voteres citizen nativity educ raceethnicity
## 1 5 metro age voteres citizen nativity educ raceethnicity
## 1 6 metro age voteres citizen nativity educ raceethnicity
## 1 7 metro age voteres citizen nativity educ raceethnicity
## 1 8 metro age voteres citizen nativity educ raceethnicity
## 1 9 metro age voteres citizen nativity educ raceethnicity
## 1 10 metro age voteres citizen nativity educ raceethnicity
## 2 1 metro age voteres citizen nativity educ raceethnicity
## 2 2 metro age voteres citizen nativity educ raceethnicity
## 2 3 metro age voteres citizen nativity educ raceethnicity
## 2 4 metro age voteres citizen nativity educ raceethnicity
## 2 5 metro age voteres citizen nativity educ raceethnicity
## 2 6 metro age voteres citizen nativity educ raceethnicity
## 2 7 metro age voteres citizen nativity educ raceethnicity
## 2 8 metro age voteres citizen nativity educ raceethnicity
## 2 9 metro age voteres citizen nativity educ raceethnicity
## 2 10 metro age voteres citizen nativity educ raceethnicity
## 3 1 metro age voteres citizen nativity educ raceethnicity
## 3 2 metro age voteres citizen nativity educ raceethnicity
## 3 3 metro age voteres citizen nativity educ raceethnicity
## 3 4 metro age voteres citizen nativity educ raceethnicity
## 3 5 metro age voteres citizen nativity educ raceethnicity
## 3 6 metro age voteres citizen nativity educ raceethnicity
## 3 7 metro age voteres citizen nativity educ raceethnicity
## 3 8 metro age voteres citizen nativity educ raceethnicity
## 3 9 metro age voteres citizen nativity educ raceethnicity
## 3 10 metro age voteres citizen nativity educ raceethnicity
## 4 1 metro age voteres citizen nativity educ raceethnicity
## 4 2 metro age voteres citizen nativity educ raceethnicity
## 4 3 metro age voteres citizen nativity educ raceethnicity
## 4 4 metro age voteres citizen nativity educ raceethnicity
## 4 5 metro age voteres citizen nativity educ raceethnicity
## 4 6 metro age voteres citizen nativity educ raceethnicity
## 4 7 metro age voteres citizen nativity educ raceethnicity
## 4 8 metro age voteres citizen nativity educ raceethnicity
## 4 9 metro age voteres citizen nativity educ raceethnicity
## 4 10 metro age voteres citizen nativity educ raceethnicity
## 5 1 metro age voteres citizen nativity educ raceethnicity
## 5 2 metro age voteres citizen nativity educ raceethnicity
## 5 3 metro age voteres citizen nativity educ raceethnicity
## 5 4 metro age voteres citizen nativity educ raceethnicity
## 5 5 metro age voteres citizen nativity educ raceethnicity
## 5 6 metro age voteres citizen nativity educ raceethnicity
## 5 7 metro age voteres citizen nativity educ raceethnicity
## 5 8 metro age voteres citizen nativity educ raceethnicity
## 5 9 metro age voteres citizen nativity educ raceethnicity
## 5 10 metro age voteres citizen nativity educ raceethnicitydat.imp <- complete(imp,action = 1)
head(dat.imp, n=10)head(cps20[, c("sex", "metro", "age", "statefip","voteres", "citizen", "nativity", "educ", "raceethnicity")], n=10)fit.metro <- with(data = imp, expr=lm(metro~factor(voteres)+age+statefip+citizen+nativity+educ+raceethnicity))## Warning in model.response(mf, "numeric"): using type = "numeric" with a factor
## response will be ignored## Warning in Ops.factor(y, z$residuals): '-' not meaningful for factors## Warning in model.response(mf, "numeric"): using type = "numeric" with a factor
## response will be ignored## Warning in Ops.factor(y, z$residuals): '-' not meaningful for factors## Warning in model.response(mf, "numeric"): using type = "numeric" with a factor
## response will be ignored## Warning in Ops.factor(y, z$residuals): '-' not meaningful for factors## Warning in model.response(mf, "numeric"): using type = "numeric" with a factor
## response will be ignored## Warning in Ops.factor(y, z$residuals): '-' not meaningful for factors## Warning in model.response(mf, "numeric"): using type = "numeric" with a factor
## response will be ignored## Warning in Ops.factor(y, z$residuals): '-' not meaningful for factors## Warning in model.response(mf, "numeric"): using type = "numeric" with a factor
## response will be ignored## Warning in Ops.factor(y, z$residuals): '-' not meaningful for factors## Warning in model.response(mf, "numeric"): using type = "numeric" with a factor
## response will be ignored## Warning in Ops.factor(y, z$residuals): '-' not meaningful for factors## Warning in model.response(mf, "numeric"): using type = "numeric" with a factor
## response will be ignored## Warning in Ops.factor(y, z$residuals): '-' not meaningful for factors## Warning in model.response(mf, "numeric"): using type = "numeric" with a factor
## response will be ignored## Warning in Ops.factor(y, z$residuals): '-' not meaningful for factors## Warning in model.response(mf, "numeric"): using type = "numeric" with a factor
## response will be ignored## Warning in Ops.factor(y, z$residuals): '-' not meaningful for factorsfit.metro## call :
## with.mids(data = imp, expr = lm(metro ~ factor(voteres) + age +
## statefip + citizen + nativity + educ + raceethnicity))
##
## call1 :
## mice(data = data2[, c("sex", "metro", "age", "statefip", "voteres",
## "citizen", "nativity", "educ", "raceethnicity")], m = 10,
## seed = 14)
##
## nmis :
## sex metro age statefip voteres
## 0 1133 24076 0 42351
## citizen nativity educ raceethnicity
## 6241 124 44096 758
##
## analyses :
## [[1]]
##
## Call:
## lm(formula = metro ~ factor(voteres) + age + statefip + citizen +
## nativity + educ + raceethnicity)
##
## Coefficients:
## (Intercept) factor(voteres)1to2yrs
## 1.8166831 0.0814883
## factor(voteres)3to4yrs factor(voteres)5+yrs
## 0.1161665 0.1571652
## age statefip2
## 0.0002099 -0.2890820
## statefip4 statefip5
## -0.2682140 -0.2381687
## statefip6 statefip8
## -0.0623784 -0.1315190
## statefip9 statefip10
## 0.2416982 -1.0325783
## statefip11 statefip12
## -0.9625658 0.2524737
## statefip13 statefip15
## 0.2278348 -0.0687227
## statefip16 statefip17
## -0.1484974 0.0585368
## statefip18 statefip19
## -0.1374662 -0.4489750
## statefip20 statefip21
## -0.1546807 -0.1662601
## statefip22 statefip23
## -0.0036488 -0.3574079
## statefip24 statefip25
## 0.4350988 0.5154131
## statefip26 statefip27
## 0.0654980 -0.0133864
## statefip28 statefip29
## 0.1954826 0.0652569
## statefip30 statefip31
## -0.4436327 -0.1857050
## statefip32 statefip33
## -0.2004603 0.2773707
## statefip34 statefip35
## 0.4557493 -0.3988667
## statefip36 statefip37
## -0.0837908 -0.1327731
## statefip38 statefip39
## -0.5758561 0.2508108
## statefip40 statefip41
## 0.0005493 -0.2287899
## statefip42 statefip44
## -0.0073929 0.6668193
## statefip45 statefip46
## 0.1350548 -0.5381834
## statefip47 statefip48
## -0.0789876 -0.0774550
## statefip49 statefip50
## 0.4698520 -0.4540805
## statefip51 statefip53
## 0.1520114 -0.0806712
## statefip54 statefip55
## -0.6463366 -0.1446852
## statefip56 citizennaturalized
## -0.3881356 0.1136993
## nativityUSborn educBA
## 0.1379269 -0.0152529
## educHS educMA
## -0.0132501 -0.0221857
## educPhD educsomeHS
## -0.0737439 -0.0192574
## educupto8thgrade raceethnicityAsian.Not Latinx
## -0.0574048 -0.0406246
## raceethnicityBlack.Not Latinx raceethnicityLatinx
## -0.2170541 -0.0560199
## raceethnicitymultiple.Not Latinx raceethnicityNHPI.Not Latinx
## -0.0285175 -0.0393567
## raceethnicityWhite.Not Latinx
## 0.0192574
##
##
## [[2]]
##
## Call:
## lm(formula = metro ~ factor(voteres) + age + statefip + citizen +
## nativity + educ + raceethnicity)
##
## Coefficients:
## (Intercept) factor(voteres)1to2yrs
## 1.872e+00 8.142e-02
## factor(voteres)3to4yrs factor(voteres)5+yrs
## 1.130e-01 1.560e-01
## age statefip2
## 9.182e-05 -2.930e-01
## statefip4 statefip5
## -2.740e-01 -2.370e-01
## statefip6 statefip8
## -6.136e-02 -1.306e-01
## statefip9 statefip10
## 2.403e-01 -1.033e+00
## statefip11 statefip12
## -9.634e-01 2.524e-01
## statefip13 statefip15
## 2.248e-01 -6.958e-02
## statefip16 statefip17
## -1.494e-01 5.940e-02
## statefip18 statefip19
## -1.356e-01 -4.501e-01
## statefip20 statefip21
## -1.533e-01 -1.668e-01
## statefip22 statefip23
## -5.572e-03 -3.562e-01
## statefip24 statefip25
## 4.332e-01 5.181e-01
## statefip26 statefip27
## 6.620e-02 -1.509e-02
## statefip28 statefip29
## 1.945e-01 6.381e-02
## statefip30 statefip31
## -4.420e-01 -1.884e-01
## statefip32 statefip33
## -1.999e-01 2.748e-01
## statefip34 statefip35
## 4.546e-01 -4.010e-01
## statefip36 statefip37
## -8.245e-02 -1.340e-01
## statefip38 statefip39
## -5.789e-01 2.507e-01
## statefip40 statefip41
## 8.990e-04 -2.295e-01
## statefip42 statefip44
## -7.355e-03 6.680e-01
## statefip45 statefip46
## 1.324e-01 -5.431e-01
## statefip47 statefip48
## -7.989e-02 -7.874e-02
## statefip49 statefip50
## 4.780e-01 -4.575e-01
## statefip51 statefip53
## 1.532e-01 -8.245e-02
## statefip54 statefip55
## -6.457e-01 -1.444e-01
## statefip56 citizennaturalized
## -3.897e-01 7.935e-02
## nativityUSborn educBA
## 1.110e-01 -2.159e-02
## educHS educMA
## -2.176e-02 -2.980e-02
## educPhD educsomeHS
## -5.451e-02 -4.828e-02
## educupto8thgrade raceethnicityAsian.Not Latinx
## -6.581e-02 -5.768e-02
## raceethnicityBlack.Not Latinx raceethnicityLatinx
## -2.326e-01 -7.200e-02
## raceethnicitymultiple.Not Latinx raceethnicityNHPI.Not Latinx
## -3.821e-02 -6.013e-02
## raceethnicityWhite.Not Latinx
## 2.831e-03
##
##
## [[3]]
##
## Call:
## lm(formula = metro ~ factor(voteres) + age + statefip + citizen +
## nativity + educ + raceethnicity)
##
## Coefficients:
## (Intercept) factor(voteres)1to2yrs
## 1.8332970 0.0726730
## factor(voteres)3to4yrs factor(voteres)5+yrs
## 0.1170331 0.1603620
## age statefip2
## -0.0001867 -0.2906769
## statefip4 statefip5
## -0.2729872 -0.2373206
## statefip6 statefip8
## -0.0628101 -0.1328806
## statefip9 statefip10
## 0.2411466 -1.0343287
## statefip11 statefip12
## -0.9640329 0.2541117
## statefip13 statefip15
## 0.2261251 -0.0656491
## statefip16 statefip17
## -0.1474902 0.0569517
## statefip18 statefip19
## -0.1353557 -0.4487319
## statefip20 statefip21
## -0.1567855 -0.1684137
## statefip22 statefip23
## -0.0029846 -0.3562287
## statefip24 statefip25
## 0.4314634 0.5135467
## statefip26 statefip27
## 0.0651385 -0.0144076
## statefip28 statefip29
## 0.1954147 0.0616418
## statefip30 statefip31
## -0.4418657 -0.1901191
## statefip32 statefip33
## -0.1985325 0.2754408
## statefip34 statefip35
## 0.4510984 -0.4001041
## statefip36 statefip37
## -0.0834429 -0.1337868
## statefip38 statefip39
## -0.5775936 0.2489222
## statefip40 statefip41
## 0.0304248 -0.2289004
## statefip42 statefip44
## -0.0072122 0.6669872
## statefip45 statefip46
## 0.1345393 -0.5398391
## statefip47 statefip48
## -0.0805583 -0.0787876
## statefip49 statefip50
## 0.4695538 -0.4570974
## statefip51 statefip53
## 0.1505660 -0.0824330
## statefip54 statefip55
## -0.6452698 -0.1424947
## statefip56 citizennaturalized
## -0.3907015 0.1044428
## nativityUSborn educBA
## 0.1298604 -0.0050173
## educHS educMA
## -0.0117985 -0.0221949
## educPhD educsomeHS
## -0.0472162 -0.0581953
## educupto8thgrade raceethnicityAsian.Not Latinx
## -0.0592953 -0.0435715
## raceethnicityBlack.Not Latinx raceethnicityLatinx
## -0.2162857 -0.0549073
## raceethnicitymultiple.Not Latinx raceethnicityNHPI.Not Latinx
## -0.0388301 -0.0451594
## raceethnicityWhite.Not Latinx
## 0.0203241
##
##
## [[4]]
##
## Call:
## lm(formula = metro ~ factor(voteres) + age + statefip + citizen +
## nativity + educ + raceethnicity)
##
## Coefficients:
## (Intercept) factor(voteres)1to2yrs
## 1.833e+00 8.029e-02
## factor(voteres)3to4yrs factor(voteres)5+yrs
## 1.111e-01 1.538e-01
## age statefip2
## -3.213e-06 -2.889e-01
## statefip4 statefip5
## -2.743e-01 -2.370e-01
## statefip6 statefip8
## -6.030e-02 -1.314e-01
## statefip9 statefip10
## 2.398e-01 -1.032e+00
## statefip11 statefip12
## -9.604e-01 2.545e-01
## statefip13 statefip15
## 2.250e-01 -6.496e-02
## statefip16 statefip17
## -1.506e-01 5.755e-02
## statefip18 statefip19
## -1.374e-01 -4.512e-01
## statefip20 statefip21
## -1.560e-01 -1.679e-01
## statefip22 statefip23
## -4.638e-03 -3.579e-01
## statefip24 statefip25
## 4.344e-01 5.170e-01
## statefip26 statefip27
## 6.564e-02 -1.427e-02
## statefip28 statefip29
## 1.948e-01 6.339e-02
## statefip30 statefip31
## -4.430e-01 -1.897e-01
## statefip32 statefip33
## -2.012e-01 2.746e-01
## statefip34 statefip35
## 4.554e-01 -3.973e-01
## statefip36 statefip37
## -8.326e-02 -1.340e-01
## statefip38 statefip39
## -5.775e-01 2.499e-01
## statefip40 statefip41
## 3.666e-03 -2.292e-01
## statefip42 statefip44
## -7.635e-03 6.662e-01
## statefip45 statefip46
## 1.343e-01 -5.446e-01
## statefip47 statefip48
## -7.958e-02 -7.650e-02
## statefip49 statefip50
## 4.762e-01 -4.555e-01
## statefip51 statefip53
## 1.533e-01 -8.050e-02
## statefip54 statefip55
## -6.483e-01 -1.459e-01
## statefip56 citizennaturalized
## -3.888e-01 1.098e-01
## nativityUSborn educBA
## 1.323e-01 -2.786e-02
## educHS educMA
## -1.666e-02 -2.739e-02
## educPhD educsomeHS
## -7.898e-02 -5.682e-02
## educupto8thgrade raceethnicityAsian.Not Latinx
## -7.934e-02 -3.765e-02
## raceethnicityBlack.Not Latinx raceethnicityLatinx
## -2.124e-01 -5.388e-02
## raceethnicitymultiple.Not Latinx raceethnicityNHPI.Not Latinx
## -2.168e-02 -4.357e-02
## raceethnicityWhite.Not Latinx
## 2.671e-02
##
##
## [[5]]
##
## Call:
## lm(formula = metro ~ factor(voteres) + age + statefip + citizen +
## nativity + educ + raceethnicity)
##
## Coefficients:
## (Intercept) factor(voteres)1to2yrs
## 1.8571264 0.0659301
## factor(voteres)3to4yrs factor(voteres)5+yrs
## 0.1021752 0.1507848
## age statefip2
## 0.0001062 -0.2903029
## statefip4 statefip5
## -0.2678946 -0.2381260
## statefip6 statefip8
## -0.0623246 -0.1316909
## statefip9 statefip10
## 0.2403824 -1.0333233
## statefip11 statefip12
## -0.9614183 0.2549104
## statefip13 statefip15
## 0.2258450 -0.0673023
## statefip16 statefip17
## -0.1474646 0.0592268
## statefip18 statefip19
## -0.1381371 -0.4503819
## statefip20 statefip21
## -0.1555074 -0.1670414
## statefip22 statefip23
## -0.0045231 -0.3557682
## statefip24 statefip25
## 0.4340493 0.5155229
## statefip26 statefip27
## 0.0653032 -0.0142771
## statefip28 statefip29
## 0.1947972 0.0639871
## statefip30 statefip31
## -0.4424302 -0.1875443
## statefip32 statefip33
## -0.1986581 0.2767818
## statefip34 statefip35
## 0.4548388 -0.4000501
## statefip36 statefip37
## -0.0852234 -0.1335885
## statefip38 statefip39
## -0.5773637 0.2493470
## statefip40 statefip41
## 0.0048619 -0.2288238
## statefip42 statefip44
## -0.0066027 0.6662871
## statefip45 statefip46
## 0.1337706 -0.5416010
## statefip47 statefip48
## -0.0787339 -0.0783753
## statefip49 statefip50
## 0.4798703 -0.4558161
## statefip51 statefip53
## 0.1534998 -0.0807436
## statefip54 statefip55
## -0.6461558 -0.1433046
## statefip56 citizennaturalized
## -0.3893780 0.0980204
## nativityUSborn educBA
## 0.1243584 -0.0319786
## educHS educMA
## -0.0276503 -0.0343926
## educPhD educsomeHS
## -0.0572686 -0.0643913
## educupto8thgrade raceethnicityAsian.Not Latinx
## -0.0650059 -0.0455960
## raceethnicityBlack.Not Latinx raceethnicityLatinx
## -0.2182956 -0.0581738
## raceethnicitymultiple.Not Latinx raceethnicityNHPI.Not Latinx
## -0.0328456 -0.0423005
## raceethnicityWhite.Not Latinx
## 0.0158774
##
##
## [[6]]
##
## Call:
## lm(formula = metro ~ factor(voteres) + age + statefip + citizen +
## nativity + educ + raceethnicity)
##
## Coefficients:
## (Intercept) factor(voteres)1to2yrs
## 1.861e+00 7.872e-02
## factor(voteres)3to4yrs factor(voteres)5+yrs
## 1.191e-01 1.525e-01
## age statefip2
## -7.073e-05 -2.909e-01
## statefip4 statefip5
## -2.693e-01 -2.379e-01
## statefip6 statefip8
## -6.081e-02 -1.333e-01
## statefip9 statefip10
## 2.401e-01 -1.035e+00
## statefip11 statefip12
## -9.648e-01 2.525e-01
## statefip13 statefip15
## 2.257e-01 -6.656e-02
## statefip16 statefip17
## -1.498e-01 5.892e-02
## statefip18 statefip19
## -1.382e-01 -4.500e-01
## statefip20 statefip21
## -1.560e-01 -1.649e-01
## statefip22 statefip23
## -4.473e-03 -3.583e-01
## statefip24 statefip25
## 4.341e-01 5.126e-01
## statefip26 statefip27
## 6.688e-02 -1.478e-02
## statefip28 statefip29
## 1.951e-01 6.314e-02
## statefip30 statefip31
## -4.436e-01 -1.901e-01
## statefip32 statefip33
## -2.011e-01 2.758e-01
## statefip34 statefip35
## 4.541e-01 -4.007e-01
## statefip36 statefip37
## -8.364e-02 -1.326e-01
## statefip38 statefip39
## -5.787e-01 2.504e-01
## statefip40 statefip41
## 9.537e-03 -2.295e-01
## statefip42 statefip44
## -6.307e-03 6.669e-01
## statefip45 statefip46
## 1.332e-01 -5.410e-01
## statefip47 statefip48
## -7.963e-02 -7.878e-02
## statefip49 statefip50
## 4.657e-01 -4.564e-01
## statefip51 statefip53
## 1.513e-01 -8.263e-02
## statefip54 statefip55
## -6.455e-01 -1.451e-01
## statefip56 citizennaturalized
## -3.884e-01 8.266e-02
## nativityUSborn educBA
## 1.112e-01 -2.081e-02
## educHS educMA
## -2.137e-02 -2.960e-02
## educPhD educsomeHS
## -6.160e-02 -5.368e-02
## educupto8thgrade raceethnicityAsian.Not Latinx
## -7.581e-02 -4.349e-02
## raceethnicityBlack.Not Latinx raceethnicityLatinx
## -2.157e-01 -5.604e-02
## raceethnicitymultiple.Not Latinx raceethnicityNHPI.Not Latinx
## -2.932e-02 -4.509e-02
## raceethnicityWhite.Not Latinx
## 2.106e-02
##
##
## [[7]]
##
## Call:
## lm(formula = metro ~ factor(voteres) + age + statefip + citizen +
## nativity + educ + raceethnicity)
##
## Coefficients:
## (Intercept) factor(voteres)1to2yrs
## 1.828e+00 8.961e-02
## factor(voteres)3to4yrs factor(voteres)5+yrs
## 1.106e-01 1.564e-01
## age statefip2
## 5.862e-05 -2.918e-01
## statefip4 statefip5
## -2.656e-01 -2.392e-01
## statefip6 statefip8
## -6.196e-02 -1.300e-01
## statefip9 statefip10
## 2.435e-01 -1.035e+00
## statefip11 statefip12
## -9.597e-01 2.538e-01
## statefip13 statefip15
## 2.240e-01 -6.905e-02
## statefip16 statefip17
## -1.493e-01 5.912e-02
## statefip18 statefip19
## -1.391e-01 -4.511e-01
## statefip20 statefip21
## -1.577e-01 -1.700e-01
## statefip22 statefip23
## -6.663e-03 -3.563e-01
## statefip24 statefip25
## 4.354e-01 5.149e-01
## statefip26 statefip27
## 6.693e-02 -1.360e-02
## statefip28 statefip29
## 1.938e-01 6.345e-02
## statefip30 statefip31
## -4.440e-01 -1.879e-01
## statefip32 statefip33
## -2.023e-01 2.761e-01
## statefip34 statefip35
## 4.551e-01 -4.004e-01
## statefip36 statefip37
## -8.328e-02 -1.339e-01
## statefip38 statefip39
## -5.788e-01 2.488e-01
## statefip40 statefip41
## 5.305e-02 -2.277e-01
## statefip42 statefip44
## -7.992e-03 6.681e-01
## statefip45 statefip46
## 1.341e-01 -5.432e-01
## statefip47 statefip48
## -8.027e-02 -7.829e-02
## statefip49 statefip50
## 4.851e-01 -4.561e-01
## statefip51 statefip53
## 1.530e-01 -8.096e-02
## statefip54 statefip55
## -6.501e-01 -1.453e-01
## statefip56 citizennaturalized
## -3.893e-01 1.136e-01
## nativityUSborn educBA
## 1.384e-01 -2.299e-02
## educHS educMA
## -1.112e-02 -3.419e-02
## educPhD educsomeHS
## -6.297e-02 -3.048e-02
## educupto8thgrade raceethnicityAsian.Not Latinx
## -4.876e-02 -4.363e-02
## raceethnicityBlack.Not Latinx raceethnicityLatinx
## -2.219e-01 -6.254e-02
## raceethnicitymultiple.Not Latinx raceethnicityNHPI.Not Latinx
## -3.358e-02 -5.003e-02
## raceethnicityWhite.Not Latinx
## 1.683e-02
##
##
## [[8]]
##
## Call:
## lm(formula = metro ~ factor(voteres) + age + statefip + citizen +
## nativity + educ + raceethnicity)
##
## Coefficients:
## (Intercept) factor(voteres)1to2yrs
## 1.8441401 0.0760031
## factor(voteres)3to4yrs factor(voteres)5+yrs
## 0.1115433 0.1507785
## age statefip2
## 0.0001435 -0.2879995
## statefip4 statefip5
## -0.2617846 -0.2375481
## statefip6 statefip8
## -0.0624407 -0.1324695
## statefip9 statefip10
## 0.2391128 -1.0345929
## statefip11 statefip12
## -0.9638961 0.2521568
## statefip13 statefip15
## 0.2238747 -0.0674361
## statefip16 statefip17
## -0.1506025 0.0576999
## statefip18 statefip19
## -0.1367409 -0.4501547
## statefip20 statefip21
## -0.1569454 -0.1687549
## statefip22 statefip23
## -0.0052101 -0.3574861
## statefip24 statefip25
## 0.4337013 0.5143754
## statefip26 statefip27
## 0.0654466 -0.0154072
## statefip28 statefip29
## 0.1939313 0.0628139
## statefip30 statefip31
## -0.4440548 -0.1870202
## statefip32 statefip33
## -0.2000997 0.2756189
## statefip34 statefip35
## 0.4528836 -0.3991052
## statefip36 statefip37
## -0.0849221 -0.1348947
## statefip38 statefip39
## -0.5778767 0.2498778
## statefip40 statefip41
## -0.0027146 -0.2292798
## statefip42 statefip44
## -0.0067368 0.6668483
## statefip45 statefip46
## 0.1330465 -0.5427817
## statefip47 statefip48
## -0.0809380 -0.0783091
## statefip49 statefip50
## 0.4634195 -0.4575264
## statefip51 statefip53
## 0.1523076 -0.0820365
## statefip54 statefip55
## -0.6450382 -0.1464282
## statefip56 citizennaturalized
## -0.3888655 0.0796096
## nativityUSborn educBA
## 0.1106755 -0.0199527
## educHS educMA
## -0.0245045 -0.0294977
## educPhD educsomeHS
## -0.0685749 -0.0312127
## educupto8thgrade raceethnicityAsian.Not Latinx
## -0.0712099 -0.0258884
## raceethnicityBlack.Not Latinx raceethnicityLatinx
## -0.2004711 -0.0400343
## raceethnicitymultiple.Not Latinx raceethnicityNHPI.Not Latinx
## -0.0129878 -0.0413660
## raceethnicityWhite.Not Latinx
## 0.0345324
##
##
## [[9]]
##
## Call:
## lm(formula = metro ~ factor(voteres) + age + statefip + citizen +
## nativity + educ + raceethnicity)
##
## Coefficients:
## (Intercept) factor(voteres)1to2yrs
## 1.878e+00 7.979e-02
## factor(voteres)3to4yrs factor(voteres)5+yrs
## 1.135e-01 1.620e-01
## age statefip2
## -8.451e-05 -2.903e-01
## statefip4 statefip5
## -2.527e-01 -2.360e-01
## statefip6 statefip8
## -5.991e-02 -1.293e-01
## statefip9 statefip10
## 2.413e-01 -1.029e+00
## statefip11 statefip12
## -9.593e-01 2.556e-01
## statefip13 statefip15
## 2.272e-01 -6.489e-02
## statefip16 statefip17
## -1.494e-01 5.944e-02
## statefip18 statefip19
## -1.344e-01 -4.500e-01
## statefip20 statefip21
## -1.527e-01 -1.661e-01
## statefip22 statefip23
## -2.906e-03 -3.561e-01
## statefip24 statefip25
## 4.349e-01 5.169e-01
## statefip26 statefip27
## 6.791e-02 -1.338e-02
## statefip28 statefip29
## 1.955e-01 6.553e-02
## statefip30 statefip31
## -4.417e-01 -1.882e-01
## statefip32 statefip33
## -1.966e-01 2.778e-01
## statefip34 statefip35
## 4.558e-01 -4.007e-01
## statefip36 statefip37
## -8.193e-02 -1.333e-01
## statefip38 statefip39
## -5.786e-01 2.509e-01
## statefip40 statefip41
## 8.512e-06 -2.266e-01
## statefip42 statefip44
## -5.297e-03 6.687e-01
## statefip45 statefip46
## 1.361e-01 -5.413e-01
## statefip47 statefip48
## -7.840e-02 -7.617e-02
## statefip49 statefip50
## 4.834e-01 -4.551e-01
## statefip51 statefip53
## 1.539e-01 -8.087e-02
## statefip54 statefip55
## -6.453e-01 -1.430e-01
## statefip56 citizennaturalized
## -3.896e-01 9.903e-02
## nativityUSborn educBA
## 1.253e-01 -3.325e-02
## educHS educMA
## -3.036e-02 -3.524e-02
## educPhD educsomeHS
## -8.123e-02 -5.054e-02
## educupto8thgrade raceethnicityAsian.Not Latinx
## -7.716e-02 -7.146e-02
## raceethnicityBlack.Not Latinx raceethnicityLatinx
## -2.449e-01 -8.422e-02
## raceethnicitymultiple.Not Latinx raceethnicityNHPI.Not Latinx
## -6.532e-02 -6.751e-02
## raceethnicityWhite.Not Latinx
## -8.139e-03
##
##
## [[10]]
##
## Call:
## lm(formula = metro ~ factor(voteres) + age + statefip + citizen +
## nativity + educ + raceethnicity)
##
## Coefficients:
## (Intercept) factor(voteres)1to2yrs
## 1.8630711 0.0820628
## factor(voteres)3to4yrs factor(voteres)5+yrs
## 0.1072978 0.1465033
## age statefip2
## 0.0002079 -0.2880973
## statefip4 statefip5
## -0.2703297 -0.2372682
## statefip6 statefip8
## -0.0609026 -0.1306693
## statefip9 statefip10
## 0.2391954 -1.0337640
## statefip11 statefip12
## -0.9628913 0.2532218
## statefip13 statefip15
## 0.2257177 -0.0686959
## statefip16 statefip17
## -0.1509011 0.0607824
## statefip18 statefip19
## -0.1354828 -0.4495356
## statefip20 statefip21
## -0.1546988 -0.1677002
## statefip22 statefip23
## -0.0051669 -0.3549038
## statefip24 statefip25
## 0.4365762 0.5157716
## statefip26 statefip27
## 0.0686164 -0.0136919
## statefip28 statefip29
## 0.1953741 0.0647205
## statefip30 statefip31
## -0.4434309 -0.1878876
## statefip32 statefip33
## -0.2007475 0.2768768
## statefip34 statefip35
## 0.4562628 -0.3981422
## statefip36 statefip37
## -0.0833134 -0.1337999
## statefip38 statefip39
## -0.5765751 0.2509604
## statefip40 statefip41
## 0.0171198 -0.2282918
## statefip42 statefip44
## -0.0065811 0.6691767
## statefip45 statefip46
## 0.1339333 -0.5408846
## statefip47 statefip48
## -0.0803329 -0.0780142
## statefip49 statefip50
## 0.4797648 -0.4561163
## statefip51 statefip53
## 0.1527563 -0.0798881
## statefip54 statefip55
## -0.6443472 -0.1449801
## statefip56 citizennaturalized
## -0.3901047 0.0645358
## nativityUSborn educBA
## 0.0994726 -0.0158815
## educHS educMA
## -0.0199354 -0.0311127
## educPhD educsomeHS
## -0.0581600 -0.0462212
## educupto8thgrade raceethnicityAsian.Not Latinx
## -0.0624844 -0.0362405
## raceethnicityBlack.Not Latinx raceethnicityLatinx
## -0.2118421 -0.0508218
## raceethnicitymultiple.Not Latinx raceethnicityNHPI.Not Latinx
## -0.0301739 -0.0353687
## raceethnicityWhite.Not Latinx
## 0.0229717fit.metro2 <- with(data = cps20, expr=lm(metro~factor(voteres)+age+statefip+citizen+nativity+educ+raceethnicity))## Warning in model.response(mf, "numeric"): using type = "numeric" with a factor
## response will be ignored## Warning in Ops.factor(y, z$residuals): '-' not meaningful for factorsfit.metro2##
## Call:
## lm(formula = metro ~ factor(voteres) + age + statefip + citizen +
## nativity + educ + raceethnicity)
##
## Coefficients:
## (Intercept) factor(voteres)1to2yrs
## 1.8047335 0.0858664
## factor(voteres)3to4yrs factor(voteres)5+yrs
## 0.1062163 0.1577809
## age statefip2
## 0.0002395 -0.2828464
## statefip4 statefip5
## -0.1643787 -0.2196363
## statefip6 statefip8
## -0.0565569 -0.1980599
## statefip9 statefip10
## 0.2553288 -1.0310151
## statefip11 statefip12
## -0.9508841 0.2171094
## statefip13 statefip15
## 0.2803561 -0.1141514
## statefip16 statefip17
## -0.1714820 0.1198645
## statefip18 statefip19
## -0.0962649 -0.4130800
## statefip20 statefip21
## -0.0898075 -0.0943646
## statefip22 statefip23
## -0.0392598 -0.3835494
## statefip24 statefip25
## 0.4850571 0.5646398
## statefip26 statefip27
## 0.0847940 -0.0330954
## statefip28 statefip29
## 0.1910688 0.0230951
## statefip30 statefip31
## -0.4203715 -0.1814064
## statefip32 statefip33
## -0.2091060 0.3210511
## statefip34 statefip35
## 0.4524356 -0.4068716
## statefip36 statefip37
## -0.0139161 -0.0779008
## statefip38 statefip39
## -0.5415526 0.2922890
## statefip40 statefip41
## 0.1103232 -0.2782931
## statefip42 statefip44
## 0.0511437 0.6870950
## statefip45 statefip46
## 0.1652603 -0.5039424
## statefip47 statefip48
## -0.1064898 -0.0468201
## statefip49 statefip50
## 0.4259868 -0.3745240
## statefip51 statefip53
## 0.0721005 -0.1438050
## statefip54 statefip55
## -0.6539790 -0.1347286
## statefip56 citizennaturalized
## -0.3782167 0.1273762
## nativityUSborn educBA
## 0.1083221 -0.0185131
## educHS educMA
## -0.0155055 -0.0245297
## educPhD educsomeHS
## -0.0666952 -0.0431279
## educupto8thgrade raceethnicityAsian.Not Latinx
## -0.1243439 -0.0111677
## raceethnicityBlack.Not Latinx raceethnicityLatinx
## -0.1974055 -0.0111975
## raceethnicitymultiple.Not Latinx raceethnicityNHPI.Not Latinx
## -0.0436507 -0.0304813
## raceethnicityWhite.Not Latinx
## 0.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