Week 13

if (!require("dplyr"))
  install.packages("dplyr")

## Loading required package: dplyr

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
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##     intersect, setdiff, setequal, union

if (!require("tidyverse"))
  install.packages("tidyverse")

## Loading required package: tidyverse

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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library(dplyr)
library(ggplot2)

mydata <- read.csv("https://raw.githubusercontent.com/drkblake/Data/main/Educ_Income_2022.csv")

# Specify the DV and IV
# NOTE: You may edit the FGP and Team variable names
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))

summary(myreg)

## 
## Call:
## lm(formula = DV ~ IV, data = mydata)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -15380.9  -5904.4    538.2   4730.4  17582.3 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 44664.55    1903.16   23.47 <0.0000000000000002 ***
## IV           1349.18      90.88   14.85 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7516 on 93 degrees of freedom
## Multiple R-squared:  0.7032, Adjusted R-squared:  0.7001 
## F-statistic: 220.4 on 1 and 93 DF,  p-value: < 0.00000000000000022

Summary: People who graduate from college, stastically have a higher income.