Midterm Work Joshua Reyna 10152020
Joshua Reyna
10/15/2020
summary(cars)## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00
a <- 88.83333
s <- 7.167
n <- 20
error <- qnorm(0.975) * s/sqrt(n)
left95 <- a-error
right95 <- a+error
left95## [1] 85.69231right95## [1] 91.97435library(haven)
PA_Mortality <- read_dta("PA_Mortality.dta")
View(PA_Mortality)Question 6a
summary(PA_Mortality$povrate , PA_Mortality$cofips)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.04873 0.09667 0.12455 0.12110 0.14199 0.24159boxplot(PA_Mortality$povrate, PA_Mortality$cofips, main= "County Poverty Rates", xlab="cofips", ylab="povrate", na.rm=T)
## Midterm Question 6c
PA_Mortality1 <- PA_Mortality %>%
transmute(
avemort = ifelse(avemort <=8, 'lowmort',
ifelse(avemort >8, 'himort', NA)),
gini = ifelse (gini <= 0.4, 'Equal',
ifelse(gini > 0.4, 'Unqual', NA)),
countyfip=cofips
)Midterm Question 6d
tab1 <- table(PA_Mortality1$avemort, PA_Mortality1$countyfip)
print(tab1)##
## 42001 42003 42005 42007 42009 42011 42013 42015 42017 42019 42021
## himort 1 1 1 1 0 1 1 1 1 1 1
## lowmort 0 0 0 0 1 0 0 0 0 0 0
##
## 42023 42025 42027 42029 42031 42033 42035 42037 42039 42041 42043
## himort 1 1 0 0 1 1 1 1 1 0 1
## lowmort 0 0 1 1 0 0 0 0 0 1 0
##
## 42045 42047 42049 42051 42053 42055 42057 42059 42061 42063 42065
## himort 1 1 1 1 1 0 1 1 0 1 1
## lowmort 0 0 0 0 0 1 0 0 1 0 0
##
## 42067 42069 42071 42073 42075 42077 42079 42081 42083 42085 42087
## himort 1 1 0 1 0 0 1 1 1 1 1
## lowmort 0 0 1 0 1 1 0 0 0 0 0
##
## 42089 42091 42093 42095 42097 42099 42101 42103 42105 42107 42109
## himort 1 0 1 0 1 1 1 0 1 1 0
## lowmort 0 1 0 1 0 0 0 1 0 0 1
##
## 42111 42113 42115 42117 42119 42121 42123 42125 42127 42129 42131
## himort 1 1 1 1 0 1 1 1 1 1 1
## lowmort 0 0 0 0 1 0 0 0 0 0 0
##
## 42133
## himort 0
## lowmort 1tab1a <- round(prop.table(tab1,2), digits = 1)
print(tab1a)##
## 42001 42003 42005 42007 42009 42011 42013 42015 42017 42019 42021
## himort 1 1 1 1 0 1 1 1 1 1 1
## lowmort 0 0 0 0 1 0 0 0 0 0 0
##
## 42023 42025 42027 42029 42031 42033 42035 42037 42039 42041 42043
## himort 1 1 0 0 1 1 1 1 1 0 1
## lowmort 0 0 1 1 0 0 0 0 0 1 0
##
## 42045 42047 42049 42051 42053 42055 42057 42059 42061 42063 42065
## himort 1 1 1 1 1 0 1 1 0 1 1
## lowmort 0 0 0 0 0 1 0 0 1 0 0
##
## 42067 42069 42071 42073 42075 42077 42079 42081 42083 42085 42087
## himort 1 1 0 1 0 0 1 1 1 1 1
## lowmort 0 0 1 0 1 1 0 0 0 0 0
##
## 42089 42091 42093 42095 42097 42099 42101 42103 42105 42107 42109
## himort 1 0 1 0 1 1 1 0 1 1 0
## lowmort 0 1 0 1 0 0 0 1 0 0 1
##
## 42111 42113 42115 42117 42119 42121 42123 42125 42127 42129 42131
## himort 1 1 1 1 0 1 1 1 1 1 1
## lowmort 0 0 0 0 1 0 0 0 0 0 0
##
## 42133
## himort 0
## lowmort 1tab2 <- table(PA_Mortality1$gini, PA_Mortality1$countyfip)
print(tab2)##
## 42001 42003 42005 42007 42009 42011 42013 42015 42017 42019 42021
## Equal 1 0 0 0 0 0 0 0 0 0 0
## Unqual 0 1 1 1 1 1 1 1 1 1 1
##
## 42023 42025 42027 42029 42031 42033 42035 42037 42039 42041 42043
## Equal 0 1 0 0 0 0 1 0 0 0 0
## Unqual 1 0 1 1 1 1 0 1 1 1 1
##
## 42045 42047 42049 42051 42053 42055 42057 42059 42061 42063 42065
## Equal 0 1 0 0 0 0 1 0 0 0 0
## Unqual 1 0 1 1 1 1 0 1 1 1 1
##
## 42067 42069 42071 42073 42075 42077 42079 42081 42083 42085 42087
## Equal 1 0 0 0 1 0 0 0 0 0 0
## Unqual 0 1 1 1 0 1 1 1 1 1 1
##
## 42089 42091 42093 42095 42097 42099 42101 42103 42105 42107 42109
## Equal 1 0 0 0 0 1 0 0 0 0 0
## Unqual 0 1 1 1 1 0 1 1 1 1 1
##
## 42111 42113 42115 42117 42119 42121 42123 42125 42127 42129 42131
## Equal 0 0 0 0 0 0 0 0 0 0 1
## Unqual 1 1 1 1 1 1 1 1 1 1 0
##
## 42133
## Equal 1
## Unqual 0tab2a <- round(prop.table(tab2,2), digits = 1)
print(tab2a)##
## 42001 42003 42005 42007 42009 42011 42013 42015 42017 42019 42021
## Equal 1 0 0 0 0 0 0 0 0 0 0
## Unqual 0 1 1 1 1 1 1 1 1 1 1
##
## 42023 42025 42027 42029 42031 42033 42035 42037 42039 42041 42043
## Equal 0 1 0 0 0 0 1 0 0 0 0
## Unqual 1 0 1 1 1 1 0 1 1 1 1
##
## 42045 42047 42049 42051 42053 42055 42057 42059 42061 42063 42065
## Equal 0 1 0 0 0 0 1 0 0 0 0
## Unqual 1 0 1 1 1 1 0 1 1 1 1
##
## 42067 42069 42071 42073 42075 42077 42079 42081 42083 42085 42087
## Equal 1 0 0 0 1 0 0 0 0 0 0
## Unqual 0 1 1 1 0 1 1 1 1 1 1
##
## 42089 42091 42093 42095 42097 42099 42101 42103 42105 42107 42109
## Equal 1 0 0 0 0 1 0 0 0 0 0
## Unqual 0 1 1 1 1 0 1 1 1 1 1
##
## 42111 42113 42115 42117 42119 42121 42123 42125 42127 42129 42131
## Equal 0 0 0 0 0 0 0 0 0 0 1
## Unqual 1 1 1 1 1 1 1 1 1 1 0
##
## 42133
## Equal 1
## Unqual 0Midterm Question 6e
PA_Mortality2 <- PA_Mortality %>%
transmute(
avemort = ifelse(avemort <=8, 'lowmort',
ifelse(avemort >8, 'himort', NA)),
gini=gini
)PA_Mortality2%>%
group_by(avemort) %>%
summarize(mean =mean(gini, na.rm=T), sd=sd(gini, na.rm=T))## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 2 x 3
## avemort mean sd
## <chr> <dbl> <dbl>
## 1 himort 0.420 0.0234
## 2 lowmort 0.422 0.0234Question 6e High Mort
a <- 0.4200577
s <- 0.02342817
n <- 67
a + 66 * s/sqrt(n)## [1] 0.6089633Question 6e Low Mort
a <- 0.4218000
s <- 0.02341612
n <- 67
error <-qt(0.975, df=n-1)*s/sqrt(n)
tleft <- a-error
tright <- a+error
tleft## [1] 0.4160884tright## [1] 0.4275116a + 66 * s/sqrt(n)## [1] 0.6106085