```{r install.packages(“colorspace”) install.packages(“readxl”) install.packages(“ggpubr”)
install.packages(“rmarkdown”) library(rmarkdown)
install.packages(“readxl”) install.packages(“ggpubr”)
library(readxl) library(ggpubr)
A4Q1 <- read_excel(“C:/Users/NITHIN KUMAR/Downloads/A4Q1.xlsx”)
ggscatter( A4Q1, x = “age”, y = “education”, add = “reg.line”, xlab = “Age”, ylab = “Education of the years” )
mean(A4Q1\(age) sd(A4Q1\)age) median(A4Q1$age)
mean(A4Q1\(education) sd(A4Q1\)education) median(A4Q1$education)
hist(A4Q1$age, main = “age”, breaks = 20, col = “light green”, border = “white”, cex.main = 1, cex.axis = 1, cex.lab = 1)
hist(A4Q1$education, main = “education”, breaks = 20, col = “light blue”, border = “white”, cex.main = 1, cex.axis = 1, cex.lab = 1)
#education #The education looks normal distributed. #The data is symmetrical. #The data has a proper bell curve.
shapiro.test(A4Q1\(age) #age # the first variable is normal (p= 0.5581) shapiro.test(A4Q1\)education) #education # the variablbe is normal (p = 0.4385)
cor.test(A4Q1\(age, A4Q1\)education, method = “pearson”) cor.test(A4Q1\(age, A4Q1\)education, method = “spearman”) # A Pearson correlation was conducted to test the relationship between age (M = 35.32634, SD = 11.45344) and education (M = 13.82705, SD = 2.595901). # There was a statistically significant relationship between the two variables, r(df) = .148, p = 9.113e-12 # The relationship was weak. # As the independent variable increased, the dependent variable increase.
}
```