1
n <- 10000
u <- runif(n)
set.seed(10)
λ <- seq(from = 0, to = 1)
z <- -(log(1-u)/λ)
head(z)
## [1] Inf 1.9543690 Inf 0.5578626 Inf 2.5915683
length(z)
## [1] 10000
hist(z,main="Exp dari Inverse Transform")
library(moments)
kurtosis(z)
## [1] NaN
skewness(z)
## [1] NaN
2a
z = read.csv("C:/Users/User/Downloads/StudentsPerformance.csv",header=TRUE)
z$total.score = rowSums(z[6:8])
z$average.score = rowMeans(z[6:8])
head(z)
## gender race.ethnicity parental.level.of.education lunch
## 1 female group B bachelor's degree standard
## 2 female group C some college standard
## 3 female group B master's degree standard
## 4 male group A associate's degree free/reduced
## 5 male group C some college standard
## 6 female group B associate's degree standard
## test.preparation.course math.score reading.score writing.score total.score
## 1 none 72 72 74 218
## 2 completed 69 90 88 247
## 3 none 90 95 93 278
## 4 none 47 57 44 148
## 5 none 76 78 75 229
## 6 none 71 83 78 232
## average.score
## 1 72.66667
## 2 82.33333
## 3 92.66667
## 4 49.33333
## 5 76.33333
## 6 77.33333
2b
library(moments)
summary(z)
## gender race.ethnicity parental.level.of.education
## Length:1000 Length:1000 Length:1000
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## lunch test.preparation.course math.score reading.score
## Length:1000 Length:1000 Min. : 0.00 Min. : 17.00
## Class :character Class :character 1st Qu.: 57.00 1st Qu.: 59.00
## Mode :character Mode :character Median : 66.00 Median : 70.00
## Mean : 66.09 Mean : 69.17
## 3rd Qu.: 77.00 3rd Qu.: 79.00
## Max. :100.00 Max. :100.00
## writing.score total.score average.score
## Min. : 10.00 Min. : 27.0 Min. : 9.00
## 1st Qu.: 57.75 1st Qu.:175.0 1st Qu.: 58.33
## Median : 69.00 Median :205.0 Median : 68.33
## Mean : 68.05 Mean :203.3 Mean : 67.77
## 3rd Qu.: 79.00 3rd Qu.:233.0 3rd Qu.: 77.67
## Max. :100.00 Max. :300.0 Max. :100.00
skewness(z[6:10])
## math.score reading.score writing.score total.score average.score
## -0.2785166 -0.2587157 -0.2890096 -0.2986083 -0.2986083
kurtosis(z[6:10])
## math.score reading.score writing.score total.score average.score
## 3.267597 2.926081 2.960808 3.119221 3.119221
2c
a = table(z$gender)
A = round(100*a/sum(a),1)
pie(a, labels = A, main = 'Diagram Lingkaran Gender',col = rainbow(length(a)))
legend('topright', c('Male', 'Female'), cex = 0.8, fill = rainbow(length(a)))
b = table(z$race.ethnicity)
B = round(100*b/sum(b),1)
pie(b, labels = B, main = 'Diagram Lingkaran Race ethnicity',col = rainbow(length(b)))
legend('topright', c('Group A', 'Group B', 'Group C', 'Group D', 'Group E'), cex = 0.8, fill = rainbow(length(b)))
c = table(z$parental.level.of.education)
C = round(100*c/sum(c),1)
pie(c, labels = C, main = 'Pie Chart of Parental Level of Education',col = rainbow(length(c)))
legend('topright', c('High School', 'Some High School', 'Some College', "Associate's Degree", "Bachelor's Degree", "Master's Degree"), cex = 0.8, fill = rainbow(length(c)))
d = table(z$lunch)
D = round(100*d/sum(d),1)
pie(d, labels = D, main = 'Pie Chart of Lunch',col = rainbow(length(d)))
legend('topright', c('Standard', 'Free/Reduced'), cex = 0.8, fill = rainbow(length(d)))
e = table(z$test.preparation.course)
E = round(100*e/sum(e),1)
pie(e, labels = E, main = 'Pie Chart of Test Preparation Course',col = rainbow(length(e)))
legend('topright', c('None', 'Completed'), cex = 0.8, fill = rainbow(length(e)))
2d
library(ggplot2)
print(rataan1 <- aggregate(total.score ~ parental.level.of.education, data = z, FUN = mean))
## parental.level.of.education total.score
## 1 associate's degree 208.7072
## 2 bachelor's degree 215.7712
## 3 high school 189.2908
## 4 master's degree 220.7966
## 5 some college 205.4292
## 6 some high school 195.3240
print(barchart <- ggplot(rataan1, aes(x = reorder(rataan1$parental.level.of.education, -rataan1$total.score), y = rataan1$total.score)) + geom_bar(stat = 'identity', color = 'black', fill = 'burly wood') + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + labs(title = 'Average Score', subtitle = 'Average Score of Parental Level of Education', y = 'Average Score', x = 'Parental Level of Education'))
## Warning: Use of `rataan1$parental.level.of.education` is discouraged. Use
## `parental.level.of.education` instead.
## Warning: Use of `rataan1$total.score` is discouraged. Use `total.score` instead.
## Use of `rataan1$total.score` is discouraged. Use `total.score` instead.
print(rataan2 <- aggregate(total.score ~ race.ethnicity, data = z, FUN = mean))
## race.ethnicity total.score
## 1 group A 188.9775
## 2 group B 196.4053
## 3 group C 201.3950
## 4 group D 207.5382
## 5 group E 218.2571
print(barchart2 <- ggplot(rataan2, aes(x = reorder(rataan2$race.ethnicity, -rataan2$total.score), y = rataan2$total.score)) + geom_bar(stat = 'identity', color = 'black', fill = 'cadet blue') + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + labs(title = 'Average Score', subtitle = 'Average Score of Race Ethnicity', y = 'Average Score', x = 'Race Ethnicity'))
## Warning: Use of `rataan2$race.ethnicity` is discouraged. Use `race.ethnicity`
## instead.
## Warning: Use of `rataan2$total.score` is discouraged. Use `total.score` instead.
## Use of `rataan2$total.score` is discouraged. Use `total.score` instead.
2e
library(ggplot2)
ggplot(z, aes(fill = gender, y = total.score, x = parental.level.of.education)) + geom_bar(position = 'stack', stat = 'identity') + ggtitle('Parental Level of Education and Gender of Total Score') + xlab('')
ggplot(z, aes(fill = gender, y = total.score, x = race.ethnicity)) + geom_bar(position = 'stack', stat = 'identity') + ggtitle('Race Ethnicity and Gender of Total Score') + xlab('')
2f
hist(z$math.score [z$gender == 'male'], main = 'Histogram Math Score (Male)', col = 'chocolate', xlab = 'Math Score (Male)', ylab = 'Frequency', border = 'black')
hist(z$math.score [z$gender == 'female'], main = 'Histogram Math Score (Female)', col = 'dark khaki', xlab = 'Math Score (Female)', ylab = 'Frequency', border = 'black')
hist(z$reading.score [z$gender == 'male'], main = 'Histogram Reading Score (Male)', col = 'dark seagreen', xlab = 'Reading Score (Male)', ylab = 'Frequency', border = 'black')
hist(z$reading.score [z$gender == 'female'], main = 'Histogram Reading Score (Female)', col = 'light blue', xlab = 'Reading Score (Female)', ylab = 'Frequency', border = 'black')
hist(z$writing.score [z$gender == 'male'], main = 'Histogram Writing Score (Male)', col = 'dim gray', xlab = 'Writing Score (Male)', ylab = 'Frequency', border = 'black')
hist(z$writing.score [z$gender == 'female'], main = 'Histogram Writing Score (Female)', col = 'maroon', xlab = 'Writing Score (Female)', ylab = 'Frequency', border = 'black')
2g
boxplot(z$math.score [z$gender == 'male'] ~ z$parental.level.of.education [z$gender == 'male'], data = z, main = 'Boxplot of Math Score on Parental Education Level (Male)', xlab = 'Parental Level of Education (Male)', ylab = 'Math Score (Male)', col = 'indian red', border = 'black')
boxplot(z$math.score [z$gender == 'female'] ~ z$parental.level.of.education [z$gender == 'female'], data = z, main = 'Boxplot Math Score Parental Education Level (Female)', xlab = 'Parental Level of Education (Female)', ylab = 'Math Score (Female)', col = 'lavender', border = 'black')
boxplot(z$reading.score [z$gender == 'male'] ~ z$parental.level.of.education [z$gender == 'male'], data = z, main = 'Boxplot of Reading Score on Parental Education Level (Male)', xlab = 'Parental Level of Education (Male)', ylab = 'Reading Score (Male)', col = 'light green', border = 'black')
boxplot(z$reading.score [z$gender == 'female'] ~ z$parental.level.of.education [z$gender == 'female'], data = z, main = 'Boxplot of Reading Score on Parental Education Level (Female)', xlab = 'Parental Level of Education (Female)', ylab = 'Reading Score (Female)', col = 'sky blue', border = 'black')
boxplot(z$writing.score [z$gender == 'male'] ~ z$parental.level.of.education [z$gender == 'male'], data = z, main = 'Boxplot of Writing Score on Parental Education Level (Male)', xlab = 'Parental Level of Education (Male)', ylab = 'Writing Score (Male)', col = 'medium purple', border = 'black')
boxplot(z$writing.score [z$gender == 'female'] ~ z$parental.level.of.education [z$gender == 'female'], data = z, main = 'Boxplot of Writing Score on Parental Education Level (Female)', xlab = 'Parental Level of Education (Female)', ylab = 'Writing Score (Female)', col = 'light steel blue', border = 'black')
2h
plot(z$math.score, z$reading.score, xlab = 'Math Score', ylab = 'Reading Score', main = 'Scatterplot of Math and Reading Score', col = 'rosy brown')
plot(z$math.score, z$writing.score, xlab = 'Math Score', ylab = 'Writing Score', main = 'Scatterplot of Math and Writing Score', col = 'slate gray')
plot(z$reading.score, z$writing.score, xlab = 'Reading Score', ylab = 'Writing Score', main = 'Scatterplot of Reading and Writing Score', col = 'yellow green')
