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library(dplyr)

student <- ‘/Users/saitejaravulapalli/Documents/IUPUI_SEM 01/Intro to Statistic in R/DATA SET/student dropout.csv’ data <- read.csv(student, sep= “;”, header = TRUE)

group by Marital status,gender,locality that is national or international

group 1 {1 – single 2 – married 3 – widower 4 – divorced 5 – facto union 6 – legally separated}

group_1 <- data %>% group_by(Marital.status)

group_count1 <- group_1 %>% summarize(count1 = n())

total_count <- nrow(data)

group_count1 <- group_count1 %>% mutate(expected_probability = count1 / total_count)

print(group_count)

anomaly_1 <- group_count1 %>% filter(expected_probability == min(expected_probability))

print(anomaly_1)

data_with_anomaly_tag <- data %>% left_join(anomaly_1, by = “Marital.status”) %>% mutate(Anomaly_Tag = ifelse(expected_probability == min(expected_probability), “Anomaly”, “Normal”))

print(data_with_anomaly_tag)

group 2 {1 – male 0 – female}

group_2 <- data %>% group_by(Gender)

group_count2 <- group_2 %>% summarize(count2 = n())

total_count <- nrow(data)

group_count2 <- group_count2 %>% mutate(expected_probability = count2 / total_count)

print(group_count2)

anomaly_2 <- group_count2 %>% filter(expected_probability == min(expected_probability))

print(anomaly_2)

data_with_anomaly_tag <- data %>% left_join(anomaly_2, by = “Gender”) %>% mutate(Anomaly_Tag = ifelse(expected_probability == min(expected_probability), “Anomaly”, “Normal”))

print(data_with_anomaly_tag)

group 3 {1 – international 0 – non-international}

group_3 <- data %>% group_by(International)

group_count3 <- group_3 %>% summarize(count3 = n())

total_count <- nrow(data)

group_count3 <- group_count3 %>% mutate(expected_probability = count3 / total_count)

print(group_count3)

anomaly_3 <- group_count3 %>% filter(expected_probability == min(expected_probability))

print(anomaly_3)

data_with_anomaly_tag <- data %>% left_join(anomaly_3, by = “International”) %>% mutate(Anomaly_Tag = ifelse(expected_probability == min(expected_probability), “Anomaly”, “Normal”))

print(data_with_anomaly_tag)

According to group 3 analysis there are only 110 international students out of around 4424 students which is very less.

According to group 2 analysis the femle students are almost twice the number of male students

According to group 1 analysis the widowers are very less interested to study and as usual the single students are very high.