Segregation and Inequality
Candace Jennings
2024-01-08
Segregation and inequality in Texas cities
r = getOption("repos")
r["CRAN"] = "https://cran.wustl.edu/"
options(repos = r)
install.packages("tidyverse")## Installing package into 'C:/Users/canda/AppData/Local/R/win-library/4.3'
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## The downloaded binary packages are in
## C:\Users\canda\AppData\Local\Temp\RtmpWMLik4\downloaded_packageslibrary(ggplot2)## Warning: package 'ggplot2' was built under R version 4.3.2library(stats)
library(car)## Loading required package: carDatalibrary(dplyr)##
## Attaching package: 'dplyr'## The following object is masked from 'package:car':
##
## recode## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(stargazer)##
## Please cite as:## Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.## R package version 5.2.3. https://CRAN.R-project.org/package=stargazerlibrary(lavaan)## Warning: package 'lavaan' was built under R version 4.3.2## This is lavaan 0.6-17
## lavaan is FREE software! Please report any bugs.cities <- read.csv('C:/Users/canda/DEM Dissertation Data/citiesALLYRS.csv', na.strings=c("NA"))
cities <- cities %>% mutate_if(is.character, as.numeric)## Warning: There were 17 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `cityname = .Primitive("as.double")(cityname)`.
## Caused by warning:
## ! NAs introduced by coercion
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 16 remaining warnings.cities$Proximity<-as.factor(ifelse(cities$Proximity==1, "In Proximity", "Not in Prox"))stargazer(cities, header=FALSE, type='text', title="Descriptive Statistics",digits=1)##
## Descriptive Statistics
## =================================================
## Statistic N Mean St. Dev. Min Max
## -------------------------------------------------
## placefp20 269 40,429.3 23,084.4 1,000 80,356
## Miles 114 6.5 5.5 0.0 21.0
## pboo_20 262 8.4 10.6 0.0 67.1
## pboo_10 240 8.6 11.0 0.05 65.6
## pboo_00 256 7.1 9.1 0.0 61.0
## pboo_90 257 6.5 8.6 0.0 60.6
## pboo_80 241 5.3 7.1 0.0 33.9
## t_20 261 75,666.4 207,759.0 10,070 2,304,580
## pb_20 261 12.1 11.9 0.03 71.3
## dbw_20 261 25.4 12.1 0.0 69.1
## t_10 240 70,884.6 194,929.2 10,127 2,099,451
## pb_10 240 11.4 12.1 0.03 69.4
## dbw_10 240 27.2 14.1 0.0 82.2
## t_00 204 68,784.7 190,685.1 10,302 1,953,631
## pb_00 204 10.2 11.0 0.03 57.3
## dbw_00 204 30.8 16.0 0.0 75.6
## t_90 180 62,715.0 167,886.9 10,023 1,630,553
## pb_90 180 9.5 10.5 0.0 60.6
## dbw_90 180 34.4 18.5 0.0 80.8
## t_80 154 60,676.7 165,936.1 10,197 1,595,138
## pb_80 154 8.6 9.8 0.01 40.1
## dbw_80 154 43.4 22.1 0.0 93.2
## gini_2020 262 0.4 0.1 0.3 0.5
## gini_10 53 0.4 0.1 0.3 0.6
## -------------------------------------------------stargazer(cities[c("pboo_20","t_20","dbw_20")], header=FALSE, type='text',
title="Descriptive Statistics 2020", digits=2,
covariate.labels=c("Black Homeownership","City Population","B-W Dissimilarity")
)##
## Descriptive Statistics 2020
## =============================================================
## Statistic N Mean St. Dev. Min Max
## -------------------------------------------------------------
## Black Homeownership 262 8.42 10.61 0.00 67.12
## City Population 261 75,666.40 207,759.00 10,070 2,304,580
## B-W Dissimilarity 261 25.38 12.07 0.00 69.14
## -------------------------------------------------------------stargazer(cities[c("pboo_10","t_10","dbw_10")], header=FALSE, type='text',
title="Descriptive Statistics 2010", digits=2,
covariate.labels=c("Black Homeownership","City Population","B-W Dissimilarity")
)##
## Descriptive Statistics 2010
## =============================================================
## Statistic N Mean St. Dev. Min Max
## -------------------------------------------------------------
## Black Homeownership 240 8.58 11.03 0.05 65.63
## City Population 240 70,884.63 194,929.20 10,127 2,099,451
## B-W Dissimilarity 240 27.19 14.07 0.00 82.22
## -------------------------------------------------------------stargazer(cities[c("pboo_00","t_00","dbw_00")], header=FALSE, type='text',
title="Descriptive Statistics 2000", digits=2,
covariate.labels=c("Black Homeownership","City Population","B-W Dissimilarity")
)##
## Descriptive Statistics 2000
## =============================================================
## Statistic N Mean St. Dev. Min Max
## -------------------------------------------------------------
## Black Homeownership 256 7.07 9.08 0.00 61.00
## City Population 204 68,784.74 190,685.10 10,302 1,953,631
## B-W Dissimilarity 204 30.84 15.98 0.00 75.64
## -------------------------------------------------------------stargazer(cities[c("pboo_90","t_90","dbw_90")], header=FALSE, type='text',
title="Descriptive Statistics 1990", digits=2,
covariate.labels=c("Black Homeownership","City Population","B-W Dissimilarity")
)##
## Descriptive Statistics 1990
## =============================================================
## Statistic N Mean St. Dev. Min Max
## -------------------------------------------------------------
## Black Homeownership 257 6.45 8.59 0.00 60.64
## City Population 180 62,714.99 167,886.90 10,023 1,630,553
## B-W Dissimilarity 180 34.40 18.49 0.00 80.76
## -------------------------------------------------------------stargazer(cities[c("pboo_80","t_80","dbw_80")], header=FALSE, type='text',
title="Descriptive Statistics 1980", digits=2,
covariate.labels=c("Black Homeownership","City Population","B-W Dissimilarity")
)##
## Descriptive Statistics 1980
## =============================================================
## Statistic N Mean St. Dev. Min Max
## -------------------------------------------------------------
## Black Homeownership 241 5.32 7.07 0.00 33.94
## City Population 154 60,676.74 165,936.10 10,197 1,595,138
## B-W Dissimilarity 154 43.36 22.13 0.00 93.17
## -------------------------------------------------------------object <- sapply(cities, function(x) sum(is.na(x)))
#Cities In Proximity = 1, Cities Not in Proximity = 0
cities$Proximity <- factor(cities$Proximity, ordered = FALSE )
# Make "Not in Proximity" the reference category
cities$Proximity <- relevel(cities$Proximity, ref = "Not in Prox")
table(cities$Proximity)##
## Not in Prox In Proximity
## 162 107Count of missing values of Percent Black Homeownership for cities 2020 (pboo_20) = 11, 2010 (pboo_10) = 31, 2000 (pboo_00) = 14, 1990 (pboo_90) = 90, 1980 (pboo_80) = 115
Racial residential segregation and proximity to freedom colonies
#The correlation between segregation and Black home ownership yielded a weak positive association in all years with a coefficient of 0.0363 in 2020, 0.0815 in 2010, and 0.2355 in 2000.
cor.test(cities$dbw_20,cities$pboo_20)##
## Pearson's product-moment correlation
##
## data: cities$dbw_20 and cities$pboo_20
## t = 0.50531, df = 259, p-value = 0.6138
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.09038165 0.15222271
## sample estimates:
## cor
## 0.03138275plot(cities$dbw_20,cities$pboo_20,pch=20,
xlab='D index',ylab='Percent Black Homeowners',
main='Cities by Residential Segregation and Black Homeownership in 2020')
cor.test(cities$dbw_10,cities$pboo_10)##
## Pearson's product-moment correlation
##
## data: cities$dbw_10 and cities$pboo_10
## t = 1.3949, df = 238, p-value = 0.1644
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.03700425 0.21423467
## sample estimates:
## cor
## 0.09004761plot(cities$dbw_10,cities$pboo_10,pch=20,
xlab='D index',ylab='Percent Black Homeowners',
main='Cities by Residential Segregation and Black Homeownership in 2010')
cor.test(cities$dbw_00,cities$pboo_00)##
## Pearson's product-moment correlation
##
## data: cities$dbw_00 and cities$pboo_00
## t = 4.093, df = 198, p-value = 6.198e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1462256 0.4024463
## sample estimates:
## cor
## 0.2793003plot(cities$dbw_00,cities$pboo_00,pch=20,
xlab='D index',ylab='Percent Black Homeowners',
main='Cities by Residential Segregation and Black Homeownership in 2000')
cor.test(cities$dbw_90,cities$pboo_90)##
## Pearson's product-moment correlation
##
## data: cities$dbw_90 and cities$pboo_90
## t = 5.3203, df = 177, p-value = 3.103e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2375754 0.4912287
## sample estimates:
## cor
## 0.3713092plot(cities$dbw_90,cities$pboo_90,pch=20,
xlab='D index',ylab='Percent Black Homeowners',
main='Cities by Residential Segregation and Black Homeownership in 1990')
cor.test(cities$dbw_80,cities$pboo_80)##
## Pearson's product-moment correlation
##
## data: cities$dbw_80 and cities$pboo_80
## t = 5.1751, df = 152, p-value = 7.102e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2438040 0.5137519
## sample estimates:
## cor
## 0.3870404plot(cities$dbw_80,cities$pboo_80,pch=20,
xlab='D index',ylab='Percent Black Homeowners',
main='Cities by Residential Segregation and Black Homeownership in 1980')
hyp20 <- ggplot(cities, aes(dbw_20, Proximity, col = dbw_20)) +
geom_point()
hyp20 + expand_limits(x=c(0,100))## Warning: Removed 8 rows containing missing values (`geom_point()`).
hyp10 <- ggplot(cities, aes(dbw_10, Proximity, col = dbw_10)) +
geom_point()
hyp10 + expand_limits(x=c(0,100))## Warning: Removed 29 rows containing missing values (`geom_point()`).
hyp00 <- ggplot(cities, aes(dbw_00, Proximity, col = dbw_00)) +
geom_point()
hyp00 + expand_limits(x=c(0,100))## Warning: Removed 65 rows containing missing values (`geom_point()`).
hyp90 <- ggplot(cities, aes(dbw_90, Proximity, col = dbw_90)) +
geom_point()
hyp90 + expand_limits(x=c(0,100))## Warning: Removed 89 rows containing missing values (`geom_point()`).
hyp80 <- ggplot(cities, aes(dbw_80, Proximity, col = dbw_80)) +
geom_point()
hyp80 + expand_limits(x=c(0,100))## Warning: Removed 115 rows containing missing values (`geom_point()`).
#The list of cities from the Diversity and Disparities project available for years 1980 - 2020 was used to conduct a binomial regression analysis. Of these cities, 107 were identified as being in proximity to a freedom colony and 165 were not.
table(cities$Proximity)##
## Not in Prox In Proximity
## 162 107#The results showed that proximity to freedom colonies INCREASED residential segregation by points in 2020. The results were statistically significant in 2000, but not in 2020, 2010, 1990, or 1980.
fit20=lm(dbw_20 ~ Proximity,cities)
summary(fit20)##
## Call:
## lm(formula = dbw_20 ~ Proximity, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.847 -9.357 -0.886 8.475 44.775
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.364 0.951 25.618 <2e-16 ***
## ProximityIn Proximity 2.625 1.529 1.717 0.0872 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.03 on 259 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.01125, Adjusted R-squared: 0.007434
## F-statistic: 2.947 on 1 and 259 DF, p-value: 0.08722fit10=lm(dbw_10 ~ Proximity,cities)
summary(fit10)##
## Call:
## lm(formula = dbw_10 ~ Proximity, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.626 -10.226 -1.895 8.190 56.010
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 26.206 1.179 22.233 <2e-16 ***
## ProximityIn Proximity 2.420 1.845 1.312 0.191
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.05 on 238 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.00718, Adjusted R-squared: 0.003008
## F-statistic: 1.721 on 1 and 238 DF, p-value: 0.1908fit00=lm(dbw_00 ~ Proximity,cities)
summary(fit00)##
## Call:
## lm(formula = dbw_00 ~ Proximity, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.194 -11.511 -1.039 10.575 41.698
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.809 1.426 20.197 <2e-16 ***
## ProximityIn Proximity 5.128 2.264 2.265 0.0246 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.82 on 202 degrees of freedom
## (65 observations deleted due to missingness)
## Multiple R-squared: 0.02478, Adjusted R-squared: 0.01995
## F-statistic: 5.132 on 1 and 202 DF, p-value: 0.02455fit90=lm(dbw_90 ~ Proximity,cities)
summary(fit90)##
## Call:
## lm(formula = dbw_90 ~ Proximity, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -36.428 -13.894 -0.428 16.099 43.493
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 32.584 1.754 18.578 <2e-16 ***
## ProximityIn Proximity 4.681 2.812 1.664 0.0978 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18.39 on 178 degrees of freedom
## (89 observations deleted due to missingness)
## Multiple R-squared: 0.01532, Adjusted R-squared: 0.009793
## F-statistic: 2.77 on 1 and 178 DF, p-value: 0.09779fit80=lm(dbw_80 ~ Proximity,cities)
summary(fit80)##
## Call:
## lm(formula = dbw_80 ~ Proximity, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -41.083 -13.257 -0.672 13.301 49.413
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 41.083 2.271 18.094 <2e-16 ***
## ProximityIn Proximity 5.835 3.638 1.604 0.111
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22.01 on 152 degrees of freedom
## (115 observations deleted due to missingness)
## Multiple R-squared: 0.01665, Adjusted R-squared: 0.01018
## F-statistic: 2.573 on 1 and 152 DF, p-value: 0.1108#Alternatively, I used Proximity as the dependent (outcome) variable. The results showed that residential segregation INCREASED proximity by 0.0224 points in 2020. The results were statistically significant. The results were statistically significant in 2020 and 2000, but not in 2010.
model20 = glm(Proximity ~ dbw_20,
data = cities,
family = binomial(link="logit"))
model10 = glm(Proximity ~ dbw_10,
data = cities,
family = binomial(link="logit"))
model00 = glm(Proximity ~ dbw_00,
data = cities,
family = binomial(link="logit"))
model90 = glm(Proximity ~ dbw_90,
data = cities,
family = binomial(link="logit"))
model80 = glm(Proximity ~ dbw_80,
data = cities,
family = binomial(link="logit"))
summary(model20)##
## Call:
## glm(formula = Proximity ~ dbw_20, family = binomial(link = "logit"),
## data = cities)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.92335 0.30305 -3.047 0.00231 **
## dbw_20 0.01806 0.01061 1.702 0.08877 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 348.37 on 260 degrees of freedom
## Residual deviance: 345.44 on 259 degrees of freedom
## (8 observations deleted due to missingness)
## AIC: 349.44
##
## Number of Fisher Scoring iterations: 4summary(model10)##
## Call:
## glm(formula = Proximity ~ dbw_10, family = binomial(link = "logit"),
## data = cities)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.706519 0.290099 -2.435 0.0149 *
## dbw_10 0.012254 0.009374 1.307 0.1912
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 324.60 on 239 degrees of freedom
## Residual deviance: 322.88 on 238 degrees of freedom
## (29 observations deleted due to missingness)
## AIC: 326.88
##
## Number of Fisher Scoring iterations: 4summary(model00)##
## Call:
## glm(formula = Proximity ~ dbw_00, family = binomial(link = "logit"),
## data = cities)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.055615 0.324821 -3.250 0.00115 **
## dbw_00 0.020365 0.009159 2.224 0.02618 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 274.09 on 203 degrees of freedom
## Residual deviance: 269.03 on 202 degrees of freedom
## (65 observations deleted due to missingness)
## AIC: 273.03
##
## Number of Fisher Scoring iterations: 4summary(model90)##
## Call:
## glm(formula = Proximity ~ dbw_90, family = binomial(link = "logit"),
## data = cities)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.936893 0.335291 -2.794 0.0052 **
## dbw_90 0.013888 0.008409 1.651 0.0986 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 240.57 on 179 degrees of freedom
## Residual deviance: 237.80 on 178 degrees of freedom
## (89 observations deleted due to missingness)
## AIC: 241.8
##
## Number of Fisher Scoring iterations: 4summary(model80)##
## Call:
## glm(formula = Proximity ~ dbw_80, family = binomial(link = "logit"),
## data = cities)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.983357 0.379342 -2.592 0.00953 **
## dbw_80 0.012146 0.007637 1.590 0.11172
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 205.92 on 153 degrees of freedom
## Residual deviance: 203.34 on 152 degrees of freedom
## (115 observations deleted due to missingness)
## AIC: 207.34
##
## Number of Fisher Scoring iterations: 4Anova(model20,
type="II",
test="LR")## Analysis of Deviance Table (Type II tests)
##
## Response: Proximity
## LR Chisq Df Pr(>Chisq)
## dbw_20 2.929 1 0.087 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Anova(model10,
type="II",
test="LR")## Analysis of Deviance Table (Type II tests)
##
## Response: Proximity
## LR Chisq Df Pr(>Chisq)
## dbw_10 1.7182 1 0.1899Anova(model00,
type="II",
test="LR")## Analysis of Deviance Table (Type II tests)
##
## Response: Proximity
## LR Chisq Df Pr(>Chisq)
## dbw_00 5.0661 1 0.0244 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Anova(model90,
type="II",
test="LR")## Analysis of Deviance Table (Type II tests)
##
## Response: Proximity
## LR Chisq Df Pr(>Chisq)
## dbw_90 2.7681 1 0.09616 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Anova(model80,
type="II",
test="LR")## Analysis of Deviance Table (Type II tests)
##
## Response: Proximity
## LR Chisq Df Pr(>Chisq)
## dbw_80 2.5792 1 0.1083Proximity to freedom colonies and homeownership
install.packages("ggcorrplot")## Installing package into 'C:/Users/canda/AppData/Local/R/win-library/4.3'
## (as 'lib' is unspecified)## package 'ggcorrplot' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\canda\AppData\Local\Temp\RtmpWMLik4\downloaded_packageslibrary(ggcorrplot)## Warning: package 'ggcorrplot' was built under R version 4.3.2library(ggplot2)#Change proximity to numeric to run correlation matrix
cities <- cities %>% mutate_if(is.character, as.numeric)
cities <- cities %>% mutate_if(is.integer, as.numeric)
cities <- cities %>% mutate_if(is.factor, as.numeric)
cities$t_20 <- log(cities$t_20)
cor_matrix <- cor(cities, use = "pairwise.complete.obs", method = "pearson") %>%
ggcorrplot(show.diag=FALSE, type="lower", lab=TRUE, lab_size=2)
print(cor_matrix)
df <- data.frame(cities)
sel20_vars <- c("pboo_20", "t_20","pb_20", "dbw_20")
df_select20 <- df[sel20_vars]
cor_matrix20 <- cor(df_select20, use = "pairwise.complete.obs", method = "pearson") %>%
ggcorrplot(show.diag=FALSE, type="lower", lab=TRUE, lab_size=4)
print(cor_matrix20)
df <- data.frame(cities)
sel80_vars <- c("pboo_80", "t_80","pb_80", "dbw_80")
df_select80 <- df[sel80_vars]
cor_matrix80 <- cor(df_select80, use = "pairwise.complete.obs", method = "pearson") %>%
ggcorrplot(show.diag=FALSE, type="lower", lab=TRUE, lab_size=4)
print(cor_matrix80)
#log total population due to outliers
hist(cities$t_20)
hist(log(cities$t_20+1), breaks=50)
hist(cities$t_10)
hist(log(cities$t_10+1), breaks=50)
hist(cities$t_00)
hist(log(cities$t_00+1), breaks=50)
hist(cities$t_90)
hist(log(cities$t_90+1), breaks=50)
hist(cities$t_80)
hist(log(cities$t_80+1), breaks=50)
#Run regression with Logged Total population on Percent Black homeownership. No signifigance for any year.
tpop20<- lm(cities$pboo_20 ~ log(cities$t_20))
summary(tpop20)##
## Call:
## lm(formula = cities$pboo_20 ~ log(cities$t_20))
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.270 -6.448 -4.056 3.111 58.063
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -17.554 16.467 -1.066 0.287
## log(cities$t_20) 11.125 7.038 1.581 0.115
##
## Residual standard error: 10.58 on 259 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.009556, Adjusted R-squared: 0.005732
## F-statistic: 2.499 on 1 and 259 DF, p-value: 0.1152coef(tpop20)["log(cities$t_20)"]/100## log(cities$t_20)
## 0.111247tpop10<- lm(cities$pboo_10 ~ log(cities$t_10))
summary(tpop10)##
## Call:
## lm(formula = cities$pboo_10 ~ log(cities$t_10))
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.303 -6.725 -4.234 3.273 56.897
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.7654 7.3455 -0.240 0.810
## log(cities$t_10) 0.9997 0.7066 1.415 0.158
##
## Residual standard error: 11.01 on 238 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.00834, Adjusted R-squared: 0.004173
## F-statistic: 2.002 on 1 and 238 DF, p-value: 0.1584coef(tpop10)["log(cities$t_10)"]/100## log(cities$t_10)
## 0.00999737tpop00<- lm(cities$pboo_00 ~ log(cities$t_00))
summary(tpop00)##
## Call:
## lm(formula = cities$pboo_00 ~ log(cities$t_00))
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.365 -6.078 -3.391 2.804 54.268
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.4256 7.0160 -0.488 0.626
## log(cities$t_00) 1.0728 0.6756 1.588 0.114
##
## Residual standard error: 9.392 on 198 degrees of freedom
## (69 observations deleted due to missingness)
## Multiple R-squared: 0.01257, Adjusted R-squared: 0.007586
## F-statistic: 2.521 on 1 and 198 DF, p-value: 0.1139coef(tpop00)["log(cities$t_00)"]/100## log(cities$t_00)
## 0.01072754tpop90<- lm(cities$pboo_90 ~ log(cities$t_90))
summary(tpop90)##
## Call:
## lm(formula = cities$pboo_90 ~ log(cities$t_90))
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.420 -6.262 -3.160 4.192 54.227
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.5214 7.3197 -0.754 0.4517
## log(cities$t_90) 1.2766 0.7115 1.794 0.0745 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.218 on 177 degrees of freedom
## (90 observations deleted due to missingness)
## Multiple R-squared: 0.01787, Adjusted R-squared: 0.01232
## F-statistic: 3.22 on 1 and 177 DF, p-value: 0.07446coef(tpop90)["log(cities$t_90)"]/100## log(cities$t_90)
## 0.01276642tpop80<- lm(cities$pboo_80 ~ log(cities$t_80))
summary(tpop80)##
## Call:
## lm(formula = cities$pboo_80 ~ log(cities$t_80))
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.515 -5.458 -2.950 4.121 28.452
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.3996 6.5234 -1.134 0.2584
## log(cities$t_80) 1.3760 0.6371 2.160 0.0324 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.697 on 152 degrees of freedom
## (115 observations deleted due to missingness)
## Multiple R-squared: 0.02978, Adjusted R-squared: 0.02339
## F-statistic: 4.665 on 1 and 152 DF, p-value: 0.03236coef(tpop80)["log(cities$t_80)"]/100## log(cities$t_80)
## 0.01376022#Run regression with Logged Total population on Percent Black population. Slight signifigance in 2020 and 1980.
tpop20B<- lm(cities$pb_20 ~ log(cities$t_20))
summary(tpop20B)##
## Call:
## lm(formula = cities$pb_20 ~ log(cities$t_20))
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.354 -8.050 -3.541 5.225 58.074
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -33.034 18.265 -1.809 0.0717 .
## log(cities$t_20) 19.325 7.806 2.476 0.0139 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.74 on 259 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.02312, Adjusted R-squared: 0.01935
## F-statistic: 6.129 on 1 and 259 DF, p-value: 0.01394coef(tpop20B)["log(cities$t_20)"]/100## log(cities$t_20)
## 0.1932485tpop10B<- lm(cities$pb_10 ~ log(cities$t_10))
summary(tpop10B)##
## Call:
## lm(formula = cities$pb_10 ~ log(cities$t_10))
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.032 -7.937 -4.269 4.124 57.746
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.2881 8.0375 -0.409 0.6828
## log(cities$t_10) 1.4183 0.7732 1.834 0.0679 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.05 on 238 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.01394, Adjusted R-squared: 0.009798
## F-statistic: 3.365 on 1 and 238 DF, p-value: 0.06785coef(tpop10B)["log(cities$t_10)"]/100## log(cities$t_10)
## 0.01418331tpop00B<- lm(cities$pb_00 ~ log(cities$t_00))
summary(tpop00B)##
## Call:
## lm(formula = cities$pb_00 ~ log(cities$t_00))
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.563 -7.499 -4.037 4.522 48.296
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.7408 8.0813 -0.587 0.5581
## log(cities$t_00) 1.4469 0.7795 1.856 0.0649 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.93 on 202 degrees of freedom
## (65 observations deleted due to missingness)
## Multiple R-squared: 0.01677, Adjusted R-squared: 0.0119
## F-statistic: 3.445 on 1 and 202 DF, p-value: 0.06489coef(tpop00B)["log(cities$t_00)"]/100## log(cities$t_00)
## 0.01446904tpop90B<- lm(cities$pb_90 ~ log(cities$t_90))
summary(tpop90B)##
## Call:
## lm(formula = cities$pb_90 ~ log(cities$t_90))
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.606 -7.087 -4.198 3.658 52.431
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.7914 8.2396 -0.703 0.4831
## log(cities$t_90) 1.4886 0.8007 1.859 0.0647 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.38 on 178 degrees of freedom
## (89 observations deleted due to missingness)
## Multiple R-squared: 0.01905, Adjusted R-squared: 0.01354
## F-statistic: 3.457 on 1 and 178 DF, p-value: 0.06465coef(tpop90B)["log(cities$t_90)"]/100## log(cities$t_90)
## 0.01488605tpop80B<- lm(cities$pb_80 ~ log(cities$t_80))
summary(tpop80B)##
## Call:
## lm(formula = cities$pb_80 ~ log(cities$t_80))
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.915 -6.934 -4.312 5.507 29.895
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -11.0056 8.2193 -1.339 0.1826
## log(cities$t_80) 1.9256 0.8027 2.399 0.0177 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.698 on 152 degrees of freedom
## (115 observations deleted due to missingness)
## Multiple R-squared: 0.03648, Adjusted R-squared: 0.03014
## F-statistic: 5.754 on 1 and 152 DF, p-value: 0.01766coef(tpop80B)["log(cities$t_80)"]/100## log(cities$t_80)
## 0.01925604#Run regression with Logged Total city population on Dissimilarity Index/segregation. Significance in all years.
tpop20Bw<- lm(cities$dbw_20 ~ log(cities$t_20))
summary(tpop20Bw)##
## Call:
## lm(formula = cities$dbw_20 ~ log(cities$t_20))
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.249 -8.383 -1.475 7.285 47.127
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -88.616 17.436 -5.082 7.15e-07 ***
## log(cities$t_20) 48.757 7.452 6.543 3.21e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.21 on 259 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.1418, Adjusted R-squared: 0.1385
## F-statistic: 42.81 on 1 and 259 DF, p-value: 3.206e-10coef(tpop20Bw)["log(cities$t_20)"]/100## log(cities$t_20)
## 0.4875671tpop10Bw<- lm(cities$dbw_10 ~ log(cities$t_10))
summary(tpop10Bw)##
## Call:
## lm(formula = cities$dbw_10 ~ log(cities$t_10))
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.292 -10.468 -1.869 9.094 59.568
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -22.0642 8.8422 -2.495 0.0133 *
## log(cities$t_10) 4.7609 0.8506 5.597 5.98e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 13.25 on 238 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.1163, Adjusted R-squared: 0.1126
## F-statistic: 31.33 on 1 and 238 DF, p-value: 5.984e-08coef(tpop10Bw)["log(cities$t_10)"]/100## log(cities$t_10)
## 0.04760936tpop00Bw<- lm(cities$dbw_00 ~ log(cities$t_00))
summary(tpop00Bw)##
## Call:
## lm(formula = cities$dbw_00 ~ log(cities$t_00))
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.161 -12.766 -0.462 11.450 48.520
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -17.146 11.352 -1.510 0.133
## log(cities$t_00) 4.650 1.095 4.247 3.31e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.35 on 202 degrees of freedom
## (65 observations deleted due to missingness)
## Multiple R-squared: 0.08196, Adjusted R-squared: 0.07741
## F-statistic: 18.03 on 1 and 202 DF, p-value: 3.31e-05coef(tpop00Bw)["log(cities$t_00)"]/100## log(cities$t_00)
## 0.04650091tpop90Bw<- lm(cities$dbw_90 ~ log(cities$t_90))
summary(tpop90Bw)##
## Call:
## lm(formula = cities$dbw_90 ~ log(cities$t_90))
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.940 -14.795 -1.112 12.803 50.992
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -24.070 14.039 -1.714 0.0882 .
## log(cities$t_90) 5.707 1.364 4.184 4.5e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.69 on 178 degrees of freedom
## (89 observations deleted due to missingness)
## Multiple R-squared: 0.08952, Adjusted R-squared: 0.08441
## F-statistic: 17.5 on 1 and 178 DF, p-value: 4.501e-05coef(tpop90Bw)["log(cities$t_90)"]/100## log(cities$t_90)
## 0.05707379tpop80Bw<- lm(cities$dbw_80 ~ log(cities$t_80))
summary(tpop80Bw)##
## Call:
## lm(formula = cities$dbw_80 ~ log(cities$t_80))
##
## Residuals:
## Min 1Q Median 3Q Max
## -35.822 -15.178 -0.748 13.757 56.499
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -53.636 17.075 -3.141 0.00202 **
## log(cities$t_80) 9.516 1.668 5.706 5.86e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.15 on 152 degrees of freedom
## (115 observations deleted due to missingness)
## Multiple R-squared: 0.1764, Adjusted R-squared: 0.171
## F-statistic: 32.56 on 1 and 152 DF, p-value: 5.862e-08coef(tpop80Bw)["log(cities$t_80)"]/100## log(cities$t_80)
## 0.09515862#Run regression with Logged Total population on Proximity. Some signifigance for all years except 2010.
#change proximity back to factor variable
cities$Proximity<-as.factor(ifelse(cities$Proximity==1, "In Proximity", "Not in Prox"))
str(cities)## 'data.frame': 269 obs. of 27 variables:
## $ placefp20: num 1000 1240 1576 1696 1852 ...
## $ cityname : num NA NA NA NA NA NA NA NA NA NA ...
## $ Proximity: Factor w/ 2 levels "In Proximity",..: 1 2 1 1 1 1 2 1 1 1 ...
## $ Miles : num NA 3 NA NA NA NA 13.7 NA NA NA ...
## $ FC.name : num NA NA NA NA NA NA NA NA NA NA ...
## $ pboo_20 : num 4.74 4.08 0.26 3.13 0.65 6.5 0.13 2.73 3.56 1.08 ...
## $ pboo_10 : num 4.73 2.53 0.19 3.31 0.57 7.01 0.19 1.49 3.69 1.53 ...
## $ pboo_00 : num 4.68 1.73 0 4.42 0.48 3.58 0 1.15 3.76 2.69 ...
## $ pboo_90 : num 5.34 6.51 0 2.63 0.72 2.68 0 0.9 4.06 2.46 ...
## $ pboo_80 : num 4.36 4.23 0 0.21 0.97 2.14 0 1.03 3.77 2.13 ...
## $ t_20 : num 11.74 9.72 9.88 9.68 9.79 ...
## $ pb_20 : num 11.36 17.364 0.2 2.406 0.732 ...
## $ dbw_20 : num 28.7 25 35.3 32.8 38.2 ...
## $ t_10 : num 117063 13056 18353 15869 19104 ...
## $ pb_10 : num 10.017 12.063 0.196 2.811 0.591 ...
## $ dbw_10 : num 34.8 21.4 55.3 36 27.6 ...
## $ t_00 : num 115930 14166 14760 13979 19010 ...
## $ pb_00 : num 9.092 9.798 0.115 5.802 0.742 ...
## $ dbw_00 : num 37.7 38.8 23.4 28.7 27.4 ...
## $ t_90 : num 106654 NA NA 11133 19788 ...
## $ pb_90 : num 6.816 NA NA 3.665 0.783 ...
## $ dbw_90 : num 38.1 NA NA 24.7 41.2 ...
## $ t_80 : num 98315 NA NA 12623 20961 ...
## $ pb_80 : num 6.584 NA NA 2.131 0.854 ...
## $ dbw_80 : num 48.2 NA NA 29.9 52.6 ...
## $ gini_2020: num 0.45 0.395 0.461 0.423 0.471 0.39 0.456 0.417 0.469 0.44 ...
## $ gini_10 : num 0.458 NA NA NA NA 0.398 NA NA 0.443 NA ...Make “Not in Proximity” the reference category
cities$Proximity <- relevel(cities$Proximity, ref = "Not in Prox")
table(cities$Proximity)##
## Not in Prox In Proximity
## 107 162tpop20Bx<- lm(as.numeric(cities$Proximity) ~ log(cities$t_20))
summary(tpop20Bx)##
## Call:
## lm(formula = as.numeric(cities$Proximity) ~ log(cities$t_20))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6832 -0.5907 0.3336 0.3861 0.5487
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.0710 0.7553 4.066 6.35e-05 ***
## log(cities$t_20) -0.6236 0.3228 -1.932 0.0545 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4854 on 259 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.01421, Adjusted R-squared: 0.0104
## F-statistic: 3.732 on 1 and 259 DF, p-value: 0.05447coef(tpop20Bx)["log(cities$t_20)"]/100## log(cities$t_20)
## -0.006235658tpop10Bx<- lm(as.numeric(cities$Proximity) ~ log(cities$t_10))
summary(tpop10Bx)##
## Call:
## lm(formula = as.numeric(cities$Proximity) ~ log(cities$t_10))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6422 -0.5838 0.3668 0.4012 0.5452
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.05794 0.32796 6.275 1.64e-09 ***
## log(cities$t_10) -0.04507 0.03155 -1.428 0.154
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4915 on 238 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.0085, Adjusted R-squared: 0.004334
## F-statistic: 2.04 on 1 and 238 DF, p-value: 0.1545coef(tpop10Bx)["log(cities$t_10)"]/100## log(cities$t_10)
## -0.0004506704tpop00Bx<- lm(as.numeric(cities$Proximity) ~ log(cities$t_00))
summary(tpop00Bx)##
## Call:
## lm(formula = as.numeric(cities$Proximity) ~ log(cities$t_00))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6796 -0.5879 0.3355 0.3868 0.6068
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.34379 0.35988 6.513 5.74e-10 ***
## log(cities$t_00) -0.07179 0.03471 -2.068 0.0399 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4866 on 202 degrees of freedom
## (65 observations deleted due to missingness)
## Multiple R-squared: 0.02073, Adjusted R-squared: 0.01588
## F-statistic: 4.276 on 1 and 202 DF, p-value: 0.03992coef(tpop00Bx)["log(cities$t_00)"]/100## log(cities$t_00)
## -0.0007178517tpop90Bx<- lm(as.numeric(cities$Proximity) ~ log(cities$t_90))
summary(tpop90Bx)##
## Call:
## lm(formula = as.numeric(cities$Proximity) ~ log(cities$t_90))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7163 -0.5875 0.3050 0.3635 0.6849
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.65422 0.38110 6.965 6.13e-11 ***
## log(cities$t_90) -0.10181 0.03703 -2.749 0.00659 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4801 on 178 degrees of freedom
## (89 observations deleted due to missingness)
## Multiple R-squared: 0.04073, Adjusted R-squared: 0.03534
## F-statistic: 7.559 on 1 and 178 DF, p-value: 0.006589coef(tpop90Bx)["log(cities$t_90)"]/100## log(cities$t_90)
## -0.001018135tpop80Bx<- lm(as.numeric(cities$Proximity) ~ log(cities$t_80))
summary(tpop80Bx)##
## Call:
## lm(formula = as.numeric(cities$Proximity) ~ log(cities$t_80))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6982 -0.5917 0.3163 0.3694 0.6419
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.53961 0.40908 6.208 4.87e-09 ***
## log(cities$t_80) -0.09117 0.03995 -2.282 0.0239 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4827 on 152 degrees of freedom
## (115 observations deleted due to missingness)
## Multiple R-squared: 0.03312, Adjusted R-squared: 0.02676
## F-statistic: 5.207 on 1 and 152 DF, p-value: 0.02389coef(tpop80Bx)["log(cities$t_80)"]/100## log(cities$t_80)
## -0.0009116513#Run regression with percent Black population on Proximity. Significance #Run regression with percent Black population on dissimilarity. Significance #Run regression with percent Black population on logged city population. Significance
proxpb20<- lm(as.numeric(Proximity)~pb_20,data=cities)
dbwpb20<- lm(dbw_20~pb_20,data=cities)
logtpb20<- lm(log(t_20)~pb_20,data=cities)
summary(proxpb20)##
## Call:
## lm(formula = as.numeric(Proximity) ~ pb_20, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8001 -0.4807 0.2202 0.3211 1.0530
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.804698 0.040063 45.046 < 2e-16 ***
## pb_20 -0.015777 0.002362 -6.678 1.47e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4516 on 259 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.1469, Adjusted R-squared: 0.1436
## F-statistic: 44.6 on 1 and 259 DF, p-value: 1.466e-10summary(dbwpb20)##
## Call:
## lm(formula = dbw_20 ~ pb_20, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.846 -9.002 -1.198 8.473 44.769
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.36207 1.06964 22.776 <2e-16 ***
## pb_20 0.08374 0.06307 1.328 0.185
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.06 on 259 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.006759, Adjusted R-squared: 0.002924
## F-statistic: 1.763 on 1 and 259 DF, p-value: 0.1855summary(logtpb20)##
## Call:
## lm(formula = log(t_20) ~ pb_20, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.12198 -0.07040 -0.02304 0.04690 0.33338
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.3235144 0.0081942 283.556 <2e-16 ***
## pb_20 0.0011962 0.0004832 2.476 0.0139 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09237 on 259 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.02312, Adjusted R-squared: 0.01935
## F-statistic: 6.129 on 1 and 259 DF, p-value: 0.01394#Run regression with dissimilarity on percent Black population. Significance
pbw20=lm(pb_20 ~ dbw_20,cities)
summary(pbw20)##
## Call:
## lm(formula = pb_20 ~ dbw_20, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.580 -8.156 -3.711 5.042 59.449
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.09991 1.70821 5.913 1.06e-08 ***
## dbw_20 0.08072 0.06080 1.328 0.185
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.84 on 259 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.006759, Adjusted R-squared: 0.002924
## F-statistic: 1.763 on 1 and 259 DF, p-value: 0.1855pbw10=lm(pb_10 ~ dbw_10,cities)
summary(pbw10)##
## Call:
## lm(formula = pb_10 ~ dbw_10, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.984 -7.739 -3.999 4.522 58.733
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.11335 1.69023 4.800 2.8e-06 ***
## dbw_10 0.12036 0.05523 2.179 0.0303 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.01 on 238 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.01956, Adjusted R-squared: 0.01544
## F-statistic: 4.749 on 1 and 238 DF, p-value: 0.0303pbw00=lm(pb_00 ~ dbw_00,cities)
summary(pbw00)##
## Call:
## lm(formula = pb_00 ~ dbw_00, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.679 -6.716 -2.667 3.797 47.517
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.32507 1.59001 2.091 0.0378 *
## dbw_00 0.22262 0.04579 4.861 2.34e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.43 on 202 degrees of freedom
## (65 observations deleted due to missingness)
## Multiple R-squared: 0.1047, Adjusted R-squared: 0.1003
## F-statistic: 23.63 on 1 and 202 DF, p-value: 2.339e-06pbw90=lm(pb_90 ~ dbw_90,cities)
summary(pbw90)##
## Call:
## lm(formula = pb_90 ~ dbw_90, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.556 -6.398 -2.256 2.475 53.338
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.09644 1.53108 1.369 0.173
## dbw_90 0.21403 0.03923 5.456 1.61e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.701 on 178 degrees of freedom
## (89 observations deleted due to missingness)
## Multiple R-squared: 0.1433, Adjusted R-squared: 0.1385
## F-statistic: 29.77 on 1 and 178 DF, p-value: 1.611e-07pbw80=lm(pb_80 ~ dbw_80,cities)
summary(pbw80)##
## Call:
## lm(formula = pb_80 ~ dbw_80, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.520 -5.913 -2.988 2.842 32.689
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.9204 1.6100 0.572 0.568
## dbw_80 0.1776 0.0331 5.367 2.94e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.059 on 152 degrees of freedom
## (115 observations deleted due to missingness)
## Multiple R-squared: 0.1593, Adjusted R-squared: 0.1538
## F-statistic: 28.8 on 1 and 152 DF, p-value: 2.939e-07#Run regression with Proximity on percent black population. Highly Significant all years.
pbx20=lm(pb_20 ~ Proximity,cities)
summary(pbx20)##
## Call:
## lm(formula = pb_20 ~ Proximity, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.565 -6.880 -2.417 3.975 53.409
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.857 1.092 16.358 < 2e-16 ***
## ProximityIn Proximity -9.311 1.394 -6.678 1.47e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.97 on 259 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.1469, Adjusted R-squared: 0.1436
## F-statistic: 44.6 on 1 and 259 DF, p-value: 1.466e-10pbx10=lm(pb_10 ~ Proximity,cities)
summary(pbx10)##
## Call:
## lm(formula = pb_10 ~ Proximity, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.785 -6.725 -2.739 3.358 52.413
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16.939 1.132 14.958 < 2e-16 ***
## ProximityIn Proximity -9.385 1.472 -6.375 9.42e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.21 on 238 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.1458, Adjusted R-squared: 0.1423
## F-statistic: 40.64 on 1 and 238 DF, p-value: 9.424e-10pbx00=lm(pb_00 ~ Proximity,cities)
summary(pbx00)##
## Call:
## lm(formula = pb_00 ~ Proximity, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.176 -5.906 -2.490 2.612 41.624
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.632 1.121 13.947 < 2e-16 ***
## ProximityIn Proximity -9.023 1.443 -6.251 2.38e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.09 on 202 degrees of freedom
## (65 observations deleted due to missingness)
## Multiple R-squared: 0.1621, Adjusted R-squared: 0.1579
## F-statistic: 39.08 on 1 and 202 DF, p-value: 2.379e-09pbx90=lm(pb_90 ~ Proximity,cities)
summary(pbx90)##
## Call:
## lm(formula = pb_90 ~ Proximity, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.264 -5.885 -2.536 4.423 45.938
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.617 1.151 12.700 < 2e-16 ***
## ProximityIn Proximity -8.440 1.472 -5.732 4.17e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.63 on 178 degrees of freedom
## (89 observations deleted due to missingness)
## Multiple R-squared: 0.1558, Adjusted R-squared: 0.1511
## F-statistic: 32.86 on 1 and 178 DF, p-value: 4.171e-08pbx80=lm(pb_80 ~ Proximity,cities)
summary(pbx80)##
## Call:
## lm(formula = pb_80 ~ Proximity, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.815 -5.591 -2.976 4.212 34.316
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13.042 1.190 10.960 < 2e-16 ***
## ProximityIn Proximity -7.242 1.523 -4.755 4.57e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.218 on 152 degrees of freedom
## (115 observations deleted due to missingness)
## Multiple R-squared: 0.1295, Adjusted R-squared: 0.1238
## F-statistic: 22.61 on 1 and 152 DF, p-value: 4.572e-06##Logistic Regression
#The models indicate that there is a positive relationship between the percent of Black homeownership and whether the city is within proximity to a freedom colony. Cities with high percent of Black homeowners are more likely to be within proximity to a freedom colony for years 2000, 2010, and 2020.
#model 1 #A multiple regression was performed starting with proximity to freedom colonies (Table 4). This variable proved to yield statistically significant results. Proximity to freedom colonies provided an increase in Black homeownership by 7.935 percentage points.
model1_20<- lm(pboo_20~Proximity,data=cities)
summary(model1_20)##
## Call:
## lm(formula = pboo_20 ~ Proximity, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.791 -5.199 -2.581 2.164 54.199
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.9214 0.9958 12.98 < 2e-16 ***
## ProximityIn Proximity -7.3177 1.2703 -5.76 2.36e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.01 on 260 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.1132, Adjusted R-squared: 0.1098
## F-statistic: 33.18 on 1 and 260 DF, p-value: 2.363e-08#model 2 - add in total popualtion logged #When the logged variable of city population was introduced in Model 2, there was a slight increase in homeownership although population was not statistically significant. Proximity remained a significant factor.
model2_20 <- lm(pboo_20~Proximity+log(t_20),data=cities)
summary(model2_20)##
## Call:
## lm(formula = pboo_20 ~ Proximity + log(t_20), data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.924 -4.983 -2.568 2.154 53.931
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.785 15.816 -0.176 0.860
## ProximityIn Proximity -7.131 1.283 -5.560 6.74e-08 ***
## log(t_20) 6.678 6.711 0.995 0.321
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.02 on 258 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.1155, Adjusted R-squared: 0.1087
## F-statistic: 16.85 on 2 and 258 DF, p-value: 1.327e-07#As established, percent of Black population is highly correlated with Black homeownership. As expected, an initial regression with this variable yielded a significant result, but only a small increase in homeownership. Due to this high correlation, proximity lost its significance when percent of Black population was introduced to the model. Therefore, this variable was omitted from the regression.
citpb20=lm(pboo_20~pb_20,cities)
citpb10=lm(pboo_10~pb_10,cities)
citpb00=lm(pboo_00~pb_00,cities)
citpb90=lm(pboo_90~pb_90,cities)
citpb80=lm(pboo_80~pb_80,cities)
summary(citpb20)##
## Call:
## lm(formula = pboo_20 ~ pb_20, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.0194 -1.2180 0.5173 1.5513 12.6654
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.9730 0.2680 -7.362 2.41e-12 ***
## pb_20 0.8585 0.0158 54.322 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.021 on 259 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.9193, Adjusted R-squared: 0.919
## F-statistic: 2951 on 1 and 259 DF, p-value: < 2.2e-16summary(citpb10)##
## Call:
## lm(formula = pboo_10 ~ pb_10, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.6954 -1.2473 0.3532 1.4362 13.3214
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.4811 0.2406 -6.155 3.17e-09 ***
## pb_10 0.8834 0.0145 60.941 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.713 on 238 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.9398, Adjusted R-squared: 0.9395
## F-statistic: 3714 on 1 and 238 DF, p-value: < 2.2e-16summary(citpb00)##
## Call:
## lm(formula = pboo_00 ~ pb_00, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.5147 -0.8487 0.2933 0.8873 14.6523
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.83415 0.23633 -3.53 0.000518 ***
## pb_00 0.82406 0.01565 52.64 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.441 on 198 degrees of freedom
## (69 observations deleted due to missingness)
## Multiple R-squared: 0.9333, Adjusted R-squared: 0.933
## F-statistic: 2771 on 1 and 198 DF, p-value: < 2.2e-16summary(citpb90)##
## Call:
## lm(formula = pboo_90 ~ pb_90, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.9046 -0.7423 0.2832 0.7728 8.9028
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.66915 0.20261 -3.303 0.00116 **
## pb_90 0.86542 0.01436 60.287 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.004 on 177 degrees of freedom
## (90 observations deleted due to missingness)
## Multiple R-squared: 0.9536, Adjusted R-squared: 0.9533
## F-statistic: 3634 on 1 and 177 DF, p-value: < 2.2e-16summary(citpb80)##
## Call:
## lm(formula = pboo_80 ~ pb_80, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9313 -0.4940 0.0426 0.3423 5.6163
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.10014 0.13794 -0.726 0.469
## pb_80 0.78014 0.01056 73.888 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.286 on 152 degrees of freedom
## (115 observations deleted due to missingness)
## Multiple R-squared: 0.9729, Adjusted R-squared: 0.9727
## F-statistic: 5459 on 1 and 152 DF, p-value: < 2.2e-16#model 3 add in segregation #In Model 3, the DI measure of residential segregation for each city was added to the regression. Residential segregation caused a decrease in homeownership by -0.029. This result was not statistically significant but provides a different outcome than the previous analysis comparing cities near and not near freedom colonies
m1a <- lm(pboo_20~Proximity,data=cities)
m2a <- lm(pboo_20~Proximity+log(t_20),data=cities)
m3a <- lm(pboo_20~Proximity+log(t_20)+dbw_20,data=cities)
stargazer(m1a, m2a, m3a,
type="text", title="Regression Results",
align=TRUE, dep.var.labels=c("Percent Black Homeownership 2020"),
covariate.labels=c("Proximity","City Population 2020","Dissimilarity Index 2020"),out="main2020.htm")##
## Regression Results
## ================================================================================================
## Dependent variable:
## -----------------------------------------------------------------------
## Percent Black Homeownership 2020
## (1) (2) (3)
## ------------------------------------------------------------------------------------------------
## Proximity -7.318*** -7.131*** -7.172***
## (1.270) (1.283) (1.288)
##
## City Population 2020 6.678 7.932
## (6.711) (7.231)
##
## Dissimilarity Index 2020 -0.026
## (0.056)
##
## Constant 12.921*** -2.785 -5.026
## (0.996) (15.816) (16.541)
##
## ------------------------------------------------------------------------------------------------
## Observations 262 261 261
## R2 0.113 0.116 0.116
## Adjusted R2 0.110 0.109 0.106
## Residual Std. Error 10.008 (df = 260) 10.021 (df = 258) 10.037 (df = 257)
## F Statistic 33.183*** (df = 1; 260) 16.848*** (df = 2; 258) 11.272*** (df = 3; 257)
## ================================================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01m1a <- lm(pboo_20~Proximity,data=cities)
m2a <- lm(pboo_20~Proximity+log(t_20),data=cities)
m3a <- lm(pboo_20~Proximity+log(t_20)+dbw_20,data=cities)
summary(m1a)##
## Call:
## lm(formula = pboo_20 ~ Proximity, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.791 -5.199 -2.581 2.164 54.199
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.9214 0.9958 12.98 < 2e-16 ***
## ProximityIn Proximity -7.3177 1.2703 -5.76 2.36e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.01 on 260 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.1132, Adjusted R-squared: 0.1098
## F-statistic: 33.18 on 1 and 260 DF, p-value: 2.363e-08summary(m2a)##
## Call:
## lm(formula = pboo_20 ~ Proximity + log(t_20), data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.924 -4.983 -2.568 2.154 53.931
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.785 15.816 -0.176 0.860
## ProximityIn Proximity -7.131 1.283 -5.560 6.74e-08 ***
## log(t_20) 6.678 6.711 0.995 0.321
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.02 on 258 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.1155, Adjusted R-squared: 0.1087
## F-statistic: 16.85 on 2 and 258 DF, p-value: 1.327e-07summary(m3a)##
## Call:
## lm(formula = pboo_20 ~ Proximity + log(t_20) + dbw_20, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.866 -4.863 -2.590 2.276 53.731
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.02590 16.54126 -0.304 0.761
## ProximityIn Proximity -7.17174 1.28753 -5.570 6.41e-08 ***
## log(t_20) 7.93160 7.23067 1.097 0.274
## dbw_20 -0.02623 0.05577 -0.470 0.639
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.04 on 257 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.1163, Adjusted R-squared: 0.106
## F-statistic: 11.27 on 3 and 257 DF, p-value: 5.684e-07m1b <- lm(pboo_10~Proximity,data=cities)
m2b <- lm(pboo_10~Proximity+log(t_10),data=cities)
m3b <- lm(pboo_10~Proximity+log(t_10)+dbw_10,data=cities)
summary(m1b)##
## Call:
## lm(formula = pboo_10 ~ Proximity, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.938 -5.166 -2.531 2.086 52.542
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13.088 1.050 12.465 < 2e-16 ***
## ProximityIn Proximity -7.623 1.365 -5.584 6.39e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.39 on 238 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.1158, Adjusted R-squared: 0.1121
## F-statistic: 31.18 on 1 and 238 DF, p-value: 6.385e-08summary(m2b)##
## Call:
## lm(formula = pboo_10 ~ Proximity + log(t_10), data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.161 -4.841 -2.446 2.067 52.513
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.1668 7.0863 0.870 0.385
## ProximityIn Proximity -7.4978 1.3709 -5.469 1.15e-07 ***
## log(t_10) 0.6618 0.6701 0.988 0.324
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.39 on 237 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.1195, Adjusted R-squared: 0.112
## F-statistic: 16.08 on 2 and 237 DF, p-value: 2.832e-07summary(m3b)##
## Call:
## lm(formula = pboo_10 ~ Proximity + log(t_10) + dbw_10, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.692 -5.202 -2.409 1.831 52.696
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.91401 7.16969 0.964 0.336
## ProximityIn Proximity -7.44166 1.37456 -5.414 1.52e-07 ***
## log(t_10) 0.49032 0.71218 0.688 0.492
## dbw_10 0.03656 0.05098 0.717 0.474
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.41 on 236 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.1214, Adjusted R-squared: 0.1102
## F-statistic: 10.87 on 3 and 236 DF, p-value: 1.03e-06m1c <- lm(pboo_00~Proximity,data=cities)
m2c <- lm(pboo_00~Proximity+log(t_00),data=cities)
m3c <- lm(pboo_00~Proximity+log(t_00)+dbw_00,data=cities)
summary(m1c)##
## Call:
## lm(formula = pboo_00 ~ Proximity, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.406 -4.122 -1.990 1.631 49.594
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.4061 0.8406 13.569 < 2e-16 ***
## ProximityIn Proximity -7.1164 1.0768 -6.609 2.27e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.406 on 254 degrees of freedom
## (13 observations deleted due to missingness)
## Multiple R-squared: 0.1467, Adjusted R-squared: 0.1434
## F-statistic: 43.67 on 1 and 254 DF, p-value: 2.267e-10summary(m2c)##
## Call:
## lm(formula = pboo_00 ~ Proximity + log(t_00), data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.294 -4.269 -2.274 1.733 49.544
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.1990 6.7760 0.915 0.361
## ProximityIn Proximity -7.0641 1.2818 -5.511 1.11e-07 ***
## log(t_00) 0.5552 0.6374 0.871 0.385
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.765 on 197 degrees of freedom
## (69 observations deleted due to missingness)
## Multiple R-squared: 0.1445, Adjusted R-squared: 0.1358
## F-statistic: 16.63 on 2 and 197 DF, p-value: 2.112e-07summary(m3c)##
## Call:
## lm(formula = pboo_00 ~ Proximity + log(t_00) + dbw_00, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.381 -4.448 -2.252 1.847 49.708
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.617206 6.640825 1.147 0.25277
## ProximityIn Proximity -6.444154 1.268515 -5.080 8.76e-07 ***
## log(t_00) -0.006646 0.648026 -0.010 0.99183
## dbw_00 0.129736 0.040949 3.168 0.00178 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.57 on 196 degrees of freedom
## (69 observations deleted due to missingness)
## Multiple R-squared: 0.1862, Adjusted R-squared: 0.1737
## F-statistic: 14.94 on 3 and 196 DF, p-value: 8.449e-09m1d <- lm(pboo_90~Proximity,data=cities)
m2d <- lm(pboo_90~Proximity+log(t_90),data=cities)
m3d <- lm(pboo_90~Proximity+log(t_90)+dbw_90,data=cities)
summary(m1d)##
## Call:
## lm(formula = pboo_90 ~ Proximity, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.564 -3.830 -2.390 2.436 50.076
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.5641 0.7947 13.294 < 2e-16 ***
## ProximityIn Proximity -6.7342 1.0167 -6.623 2.07e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.947 on 255 degrees of freedom
## (12 observations deleted due to missingness)
## Multiple R-squared: 0.1468, Adjusted R-squared: 0.1434
## F-statistic: 43.87 on 1 and 255 DF, p-value: 2.069e-10summary(m2d)##
## Call:
## lm(formula = pboo_90 ~ Proximity + log(t_90), data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.459 -4.348 -2.059 2.303 49.229
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.3461 7.1726 0.885 0.377
## ProximityIn Proximity -7.1279 1.3429 -5.308 3.31e-07 ***
## log(t_90) 0.5418 0.6767 0.801 0.424
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.582 on 176 degrees of freedom
## (90 observations deleted due to missingness)
## Multiple R-squared: 0.1534, Adjusted R-squared: 0.1438
## F-statistic: 15.94 on 2 and 176 DF, p-value: 4.331e-07summary(m3d)##
## Call:
## lm(formula = pboo_90 ~ Proximity + log(t_90) + dbw_90, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.289 -4.693 -1.687 2.367 50.433
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.87842 6.76773 1.460 0.146
## ProximityIn Proximity -6.73156 1.26268 -5.331 2.98e-07 ***
## log(t_90) -0.40265 0.66266 -0.608 0.544
## dbw_90 0.17106 0.03429 4.988 1.46e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.053 on 175 degrees of freedom
## (90 observations deleted due to missingness)
## Multiple R-squared: 0.2588, Adjusted R-squared: 0.2461
## F-statistic: 20.36 on 3 and 175 DF, p-value: 2.291e-11m1e <- lm(pboo_80~Proximity,data=cities)
m2e <- lm(pboo_80~Proximity+log(t_80),data=cities)
m3e <- lm(pboo_80~Proximity+log(t_80)+dbw_80,data=cities)
summary(m1e)##
## Call:
## lm(formula = pboo_80 ~ Proximity, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.199 -3.475 -2.315 2.175 29.635
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.1991 0.6902 11.880 < 2e-16 ***
## ProximityIn Proximity -4.7242 0.8837 -5.346 2.1e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.691 on 239 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 0.1068, Adjusted R-squared: 0.1031
## F-statistic: 28.58 on 1 and 239 DF, p-value: 2.099e-07summary(m2e)##
## Call:
## lm(formula = pboo_80 ~ Proximity + log(t_80), data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.256 -4.013 -2.072 3.440 28.003
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.4051 6.3879 0.220 0.826
## ProximityIn Proximity -5.7188 1.2114 -4.721 5.32e-06 ***
## log(t_80) 0.8547 0.6068 1.408 0.161
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.209 on 151 degrees of freedom
## (115 observations deleted due to missingness)
## Multiple R-squared: 0.1546, Adjusted R-squared: 0.1434
## F-statistic: 13.8 on 2 and 151 DF, p-value: 3.125e-06summary(m3e)##
## Call:
## lm(formula = pboo_80 ~ Proximity + log(t_80) + dbw_80, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.147 -4.408 -1.674 2.692 27.227
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.72641 6.14410 1.258 0.211
## ProximityIn Proximity -5.40689 1.13826 -4.750 4.71e-06 ***
## log(t_80) -0.32359 0.62305 -0.519 0.604
## dbw_80 0.12681 0.02727 4.650 7.22e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.762 on 150 degrees of freedom
## (115 observations deleted due to missingness)
## Multiple R-squared: 0.2611, Adjusted R-squared: 0.2463
## F-statistic: 17.67 on 3 and 150 DF, p-value: 7.136e-10m1a <- lm(pboo_20~Proximity,data=cities)
m2a <- lm(pboo_20~Proximity+log(t_20),data=cities)
m3a <- lm(pboo_20~Proximity+log(t_20)+dbw_20,data=cities)
m1b <- lm(pboo_10~Proximity,data=cities)
m2b <- lm(pboo_10~Proximity+log(t_10),data=cities)
m3b <- lm(pboo_10~Proximity+log(t_10)+dbw_10,data=cities)
m1c <- lm(pboo_00~Proximity,data=cities)
m2c <- lm(pboo_00~Proximity+log(t_00),data=cities)
m3c <- lm(pboo_00~Proximity+log(t_00)+dbw_00,data=cities)
m1d <- lm(pboo_90~Proximity,data=cities)
m2d <- lm(pboo_90~Proximity+log(t_90),data=cities)
m3d <- lm(pboo_90~Proximity+log(t_90)+dbw_90,data=cities)
m1e <- lm(pboo_80~Proximity,data=cities)
m2e <- lm(pboo_80~Proximity+log(t_80),data=cities)
m3e <- lm(pboo_80~Proximity+log(t_80)+dbw_80,data=cities)