1 Giriş

##  bir rastgele sayı oluşturun
set.seed(41)
x <- runif(1, 0, 10)
if(x > 3) {
        y <- 10
} else {
        y <- 0
}
x;y
## [1] 2.134905
## [1] 0
y <- if(x > 3) {
        10
} else { 
        0
}
x <-75
if(x>=65){
print("Basarılı")
}
## [1] "Basarılı"
x <-60
# Başarılı Durum
if(x>=65){
print("Basarılı")
}else{
print("Basarisiz")
}
## [1] "Basarisiz"
x <- 75 # Başarılı Durum
if(x>=90){
print("AA")
}else if(x>=80){
print("BA")
}else if(x>=70){
print("BB")
}else if(x>=65){
print("CB")
}else if(x>=60){
print("CC")
}else if(x>=50){
print("DD")
}else if(x>=30){
print("FD")
}else{
print("FF")
}
## [1] "BB"

2 Deneme

library(tuev)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
timss_2019 <- TIMSS19_btmturm7
data <- cbind(
  gender = timss_2019$BTBG02,
  select(timss_2019, starts_with("BTBG07")))
data <- haven::zap_labels(data) %>% na.omit()
data <- cbind(
  gender = data[,1],
  toplam = rowSums(data[,2:9]))

resampled_boys <- list()
resampled_girls <- list()
p_values <- numeric(100)  
set.seed(42) 
n_rep <- 100
for (i in 1:n_rep) {
  resampled_boys[[i]] <- sample(subset(data, data[,1]== 1)[, -1], size = 100, replace = TRUE)
  resampled_girls[[i]] <- sample(subset(data, data[,1] == 2)[, -1], size = 100, replace = TRUE)
  t_test_result <- t.test(resampled_boys[[i]], resampled_girls[[i]])
  p_values[i] <- t_test_result$p.value
}
significant_p_values <- p_values[p_values < 0.05]
cat("Ratio of significant p-values (p < 0.05):", length(significant_p_values)/100, "\n")
## Ratio of significant p-values (p < 0.05): 0.21