rm(list = ls()) data <- read_excel(file.choose()) data str(data) summary(data) colnames(data)

MINGGU 1 — PERSIAPAN & IMPORT DATA

rm(list = ls())

library(readxl) library(dplyr)

data <- read_excel(file.choose())

str(data) summary(data)

MINGGU 2 — STATISTIKA DESKRIPTIF

ringkasan <- data %>% summarise( n = n(), mean_uts = mean(nilai_uts), median_uts = median(nilai_uts), sd_uts = sd(nilai_uts), min_uts = min(nilai_uts), max_uts = max(nilai_uts),

mean_uas = mean(nilai_uas),
median_uas = median(nilai_uas),
sd_uas = sd(nilai_uas),
min_uas = min(nilai_uas),
max_uas = max(nilai_uas)

)

print(ringkasan)

HISTOGRAM UTS

hist(data$nilai_uts, main=“Histogram Nilai UTS”, xlab=“Nilai UTS”)

HISTOGRAM UAS

hist(data$nilai_uas, main=“Histogram Nilai UAS”, xlab=“Nilai UAS”)

#BLOXPLOT boxplot(data$nilai_uts, main=“Boxplot Nilai UTS”, ylab=“Nilai”)

boxplot(data$nilai_uas, main=“Boxplot Nilai UAS”, ylab=“Nilai”)

SCATTER PLOT (UTS vs UAS)

plot(data\(nilai_uts, data\)nilai_uas, main=“Hubungan Nilai UTS dan UAS”, xlab=“Nilai UTS”, ylab=“Nilai UAS”)

MINGGU 3 — PROBABILITAS EMPIRIS

ambang <- 75

p_uts <- mean(data\(nilai_uts > ambang) p_uas <- mean(data\)nilai_uas > ambang)

prob_empiris <- data.frame( kejadian = c(“UTS > 75”, “UAS > 75”), peluang = c(p_uts, p_uas) )

print(prob_empiris)

MINGGU 4 — PMF DISKRIT (JAM BELAJAR)

freq_jam <- table(data$jam_belajar_per_minggu) pmf_jam <- prop.table(freq_jam)

pmf_df <- data.frame( jam_belajar = as.numeric(names(pmf_jam)), probabilitas = as.numeric(pmf_jam) )

print(pmf_df)

barplot(pmf_jam, main=“PMF Jam Belajar per Minggu”, xlab=“Jam Belajar”, ylab=“Probabilitas”)

MINGGU 5 — NORMAL APROKSIMASI (NILAI UAS)

mu <- mean(data\(nilai_uas) sigma <- sd(data\)nilai_uas)

p_norm <- 1 - pnorm(75, mean = mu, sd = sigma)

model_normal <- data.frame( distribusi = “Normal Aproksimasi”, mean = mu, sd = sigma, peluang_UAS_gt_75 = p_norm )

print(model_normal)

Overlay kurva normal

hist(data$nilai_uas, probability = TRUE, main=“Histogram + Kurva Normal (UAS)”, xlab=“Nilai UAS”)

curve(dnorm(x, mean = mu, sd = sigma), add = TRUE, lwd = 2)

MINGGU 6 — DISTRIBUSI SAMPLING

set.seed(123)

B <- 2000 n1 <- 10 n2 <- 30

mean_n10 <- replicate(B, mean(sample(data\(nilai_uas, n1, replace = TRUE))) mean_n30 <- replicate(B, mean(sample(data\)nilai_uas, n2, replace = TRUE)))

hist(mean_n10, main=“Distribusi Sampling Mean (n=10)”, xlab=“Mean Sampel”)

hist(mean_n30, main=“Distribusi Sampling Mean (n=30)”, xlab=“Mean Sampel”)

MINGGU 7 — ESTIMASI PARAMETER (CI 95%)

mean_uas <- mean(data\(nilai_uas) ci_uas <- t.test(data\)nilai_uas, conf.level = 0.95)$conf.int

mean_uts <- mean(data\(nilai_uts) ci_uts <- t.test(data\)nilai_uts, conf.level = 0.95)$conf.int

estimasi <- data.frame( parameter = c(“Mean UTS”, “Mean UAS”), estimasi_titik = c(mean_uts, mean_uas), CI95_bawah = c(ci_uts[1], ci_uas[1]), CI95_atas = c(ci_uts[2], ci_uas[2]) )

print(estimasi)