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inequity_vaccine_22Jan2021.Rmd
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inequity_vaccine_22Jan2021.Rmd
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---
title: "Inequity in Vaccine Distribution"
author: "Erika Crispo, Pace University"
date: "1/22/2021"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Data from 1/20/2021
The data were provided by Emily Beckman via Givitas.
```{r }
dat<-read.csv(file.choose(),header=T)
dat
summary(dat)
```
## Is there a relationship between how many first doses were administered and county population size?
```{r }
model1<-lm(dat$Vaccine1PerPerson~dat$Population)
plot(dat$Vaccine1PerPerson~dat$Population,xlab="County population size",ylab= "First doses per capita")
abline(model1)
summary(model1)
```
We can see that there IS a significant negative relationship between county population size and the per capital number of first vaccine doses administered.
The P value is 0.00691 which means that if we repeated this 'experiment' many times, there is a 0.691% chance we'd get these results based on chance alone. We assume a significant relationship if this chance is 5% or less.
The R^2 value is 0.2876 which means that 28.76% of the pattern in the per capital first doses administered can be explained by the county population size.
## Is there a relationship between how many total vaccine doses were administered and county population size?
```{r}
model2<-lm(dat$TotalVaccinesPerPerson~dat$Population)
plot(dat$TotalVaccinesPerPerson~dat$Population,xlab="County population size",ylab= "Total doses per capita")
abline(model2)
summary(model2)
```
We can see that there IS a significant negative relationship between county population size and the per capita total number of vaccine doses administered.
The P value is 0.0132 which means that if we repeated this 'experiment' many times, there is a 1.3% chance we'd get these results based on chance alone. We assume a significant relationship if this chance is 5% or less.
The R^2 value is 0.2481 which means that 24.81% of the pattern in the per capita total doses administered can be explained by county population size.
## Is there a relationship between how many first doses were administered and the percentage of people in the county who are black?
```{r }
model3<-lm(dat$Vaccine1PerPerson~dat$PercentBlack)
plot(dat$Vaccine1PerPerson~dat$PercentBlack,xlab="% black",ylab= "First doses per capita")
abline(model3)
summary(model3)
```
We can see that there IS a significant negative relationship between the percentage of people who are black and the per capita number of first vaccine doses administered.
The P value is 0.00476 which means that if we repeated this 'experiment' many times, there is a 0.476% chance we'd get these results based on chance alone. We assume a significant relationship if this chance is 5% or less.
The R^2 value is 0.3094 which means that 30.94% of the pattern in the per capita first doses administered can be explained by the percentage of people in the county who are black.
## Is there a relationship between how many total doses were administered and the percentage of people in the county who are black?
```{r }
model4<-lm(dat$TotalVaccinesPerPerson~dat$PercentBlack)
plot(dat$TotalVaccinesPerPerson~dat$PercentBlack,xlab="% black",ylab= "Total doses per capita")
abline(model4)
summary(model4)
```
We can see that there IS a significant negative relationship between the percentage of people who are black and the per capita number of total vaccine doses administered.
The P value is 0.00476 which means that if we repeated this 'experiment' many times, there is a 0.476% chance we'd get these results based on chance alone. We assume a significant relationship if this chance is 5% or less.
The R^2 value is 0.3094 which means that 30.94% of the pattern in the per capita total doses administered can be explained by the percentage of people in the county who are black.
## Is there a relationship between how many first doses were administered and the percentage of people in the county who are white?
```{r }
model5<-lm(dat$Vaccine1PerPerson~dat$PercentWhite)
plot(dat$Vaccine1PerPerson~dat$PercentWhite,xlab="% white",ylab= "First doses per capita")
abline(model5)
summary(model5)
```
We can see that there IS a significant positive relationship between the percentage of people who are white and the per capita number of first vaccine doses administered.
The P value is 0.00196 which means that if we repeated this 'experiment' many times, there is a 0.196% chance we'd get these results based on chance alone. We assume a significant relationship if this chance is 5% or less.
The R^2 value is 0.3595 which means that 35.95% of the pattern in the per capita first doses administered can be explained by the percentage of people in the county who are white.
## Is there a relationship between how many total doses were administered and the percentage of people in the county who are white?
```{r }
model6<-lm(dat$TotalVaccinesPerPerson~dat$PercentWhite)
plot(dat$TotalVaccinesPerPerson~dat$PercentWhite,xlab="% white",ylab= "Total doses per capita")
abline(model6)
summary(model6)
```
We can see that there IS a significant positive relationship between the percentage of people who are white and the per capita total number of vaccine doses administered.
The P value is 0.00531 which means that if we repeated this 'experiment' many times, there is a 0.531 chance we'd get these results based on chance alone. We assume a significant relationship if this chance is 5% or less.
The R^2 value is 0.3031 which means that 30.31% of the pattern in the per capita total doses administered can be explained by the percentage of people in the county who are white.