Homework 3 9.24.25 - Michelle Vigil

1. Load New Data

# Load CSV or Excel file
library(readxl)
district <- read_excel("C:/Users/miche/OneDrive/Desktop/My Class Stuff/Wednesday Class/District Data/district.xls")
View(district)
# Subset relevant variables
spec_ed <- district %>% 
  select(DISTNAME, DPETSPEP, DPFPASPEP)

2. Summary

summary(spec_ed$DPETSPEP)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.00    9.90   12.10   12.27   14.20   51.70

summary(spec_ed$DPFPASPEP)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   5.800   8.900   9.711  12.500  49.000       5

3. Vaariable with missing values is : DFPASPEP now with 1202 had 1207 before.

colSums(is.na(spec_ed))

##  DISTNAME  DPETSPEP DPFPASPEP 
##         0         0         5

4. Remove Missing Values: DFPASPEP now with 1202 had 1207 before

spec_ed_clean <- spec_ed %>% drop_na()
nrow(spec_ed_clean)

## [1] 1202

5. Scatter Plot

ggplot(spec_ed_clean, aes(x = DPETSPEP, y = DPFPASPEP)) +
  geom_point(color = "red", alpha = 0.6) +
  labs(title = "Percent Special Education vs. Money Spent on Special Education",
       x = "Percent Special Education (DPETSPEP)",
       y = "Money Spent on Special Education (DPFPASPEP)")

6. Correlation is .37 and do not seem to be correlated.

cor(spec_ed_clean$DPETSPEP, spec_ed_clean$DPFPASPEP)

## [1] 0.3700234

7. Interpretation of the data

The correlation of approximately 0.37 between the percentage of special education students (DPETSPEP) and the percentage of funding allocated to special education (DPFPASPEP) shows that there is a moderate association but propbobly not direct association. Other variables like district size, available resources, state & federal funding and and other factors can help with the outcome.