if (!require("tidyverse"))
  install.packages("tidyverse")
## Loading required package: tidyverse
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## ✖ dplyr::filter() masks stats::filter()
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(tidyverse)


mydata <- read.csv("https://raw.githubusercontent.com/drkblake/Data/main/Educ_Income_2022.csv")
head(mydata,10)
##    GEOID          County     State PctCollege FamIncome
## 1  47001 Anderson County Tennessee       24.5     75637
## 2  47003  Bedford County Tennessee       17.1     71159
## 3  47005   Benton County Tennessee       11.1     65800
## 4  47007  Bledsoe County Tennessee       10.3     59695
## 5  47009   Blount County Tennessee       26.0     85194
## 6  47011  Bradley County Tennessee       23.9     75270
## 7  47013 Campbell County Tennessee       12.9     61629
## 8  47015   Cannon County Tennessee       17.5     71000
## 9  47017  Carroll County Tennessee       19.8     68542
## 10 47019   Carter County Tennessee       21.1     61776
mydata$DV <- mydata$FamIncome
mydata$IV <- mydata$PctCollege
ggplot(mydata, aes(x = DV)) + geom_histogram(color = "black", fill = "#1f78b4")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(mydata, aes(x = IV)) + geom_histogram(color = "black", fill = "#1f78b4")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

options(scipen = 999)
myreg <- lm(DV ~ IV,
            data = mydata)
plot(mydata$IV, mydata$DV)
abline(lm(mydata$DV ~ mydata$IV))

According to the scatterplot from the Tennessee data ended up with a trendline that shows the higher a county’s college education level, the higher the average household income. However, this is just a comparison of two variables and should be taken with a grain of salt.