# a menghitung statistik deskriptif
data("airquality")
airquality
## Ozone Solar.R Wind Temp Month Day
## 1 41 190 7.4 67 5 1
## 2 36 118 8.0 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 5 4
## 5 NA NA 14.3 56 5 5
## 6 28 NA 14.9 66 5 6
## 7 23 299 8.6 65 5 7
## 8 19 99 13.8 59 5 8
## 9 8 19 20.1 61 5 9
## 10 NA 194 8.6 69 5 10
## 11 7 NA 6.9 74 5 11
## 12 16 256 9.7 69 5 12
## 13 11 290 9.2 66 5 13
## 14 14 274 10.9 68 5 14
## 15 18 65 13.2 58 5 15
## 16 14 334 11.5 64 5 16
## 17 34 307 12.0 66 5 17
## 18 6 78 18.4 57 5 18
## 19 30 322 11.5 68 5 19
## 20 11 44 9.7 62 5 20
## 21 1 8 9.7 59 5 21
## 22 11 320 16.6 73 5 22
## 23 4 25 9.7 61 5 23
## 24 32 92 12.0 61 5 24
## 25 NA 66 16.6 57 5 25
## 26 NA 266 14.9 58 5 26
## 27 NA NA 8.0 57 5 27
## 28 23 13 12.0 67 5 28
## 29 45 252 14.9 81 5 29
## 30 115 223 5.7 79 5 30
## 31 37 279 7.4 76 5 31
## 32 NA 286 8.6 78 6 1
## 33 NA 287 9.7 74 6 2
## 34 NA 242 16.1 67 6 3
## 35 NA 186 9.2 84 6 4
## 36 NA 220 8.6 85 6 5
## 37 NA 264 14.3 79 6 6
## 38 29 127 9.7 82 6 7
## 39 NA 273 6.9 87 6 8
## 40 71 291 13.8 90 6 9
## 41 39 323 11.5 87 6 10
## 42 NA 259 10.9 93 6 11
## 43 NA 250 9.2 92 6 12
## 44 23 148 8.0 82 6 13
## 45 NA 332 13.8 80 6 14
## 46 NA 322 11.5 79 6 15
## 47 21 191 14.9 77 6 16
## 48 37 284 20.7 72 6 17
## 49 20 37 9.2 65 6 18
## 50 12 120 11.5 73 6 19
## 51 13 137 10.3 76 6 20
## 52 NA 150 6.3 77 6 21
## 53 NA 59 1.7 76 6 22
## 54 NA 91 4.6 76 6 23
## 55 NA 250 6.3 76 6 24
## 56 NA 135 8.0 75 6 25
## 57 NA 127 8.0 78 6 26
## 58 NA 47 10.3 73 6 27
## 59 NA 98 11.5 80 6 28
## 60 NA 31 14.9 77 6 29
## 61 NA 138 8.0 83 6 30
## 62 135 269 4.1 84 7 1
## 63 49 248 9.2 85 7 2
## 64 32 236 9.2 81 7 3
## 65 NA 101 10.9 84 7 4
## 66 64 175 4.6 83 7 5
## 67 40 314 10.9 83 7 6
## 68 77 276 5.1 88 7 7
## 69 97 267 6.3 92 7 8
## 70 97 272 5.7 92 7 9
## 71 85 175 7.4 89 7 10
## 72 NA 139 8.6 82 7 11
## 73 10 264 14.3 73 7 12
## 74 27 175 14.9 81 7 13
## 75 NA 291 14.9 91 7 14
## 76 7 48 14.3 80 7 15
## 77 48 260 6.9 81 7 16
## 78 35 274 10.3 82 7 17
## 79 61 285 6.3 84 7 18
## 80 79 187 5.1 87 7 19
## 81 63 220 11.5 85 7 20
## 82 16 7 6.9 74 7 21
## 83 NA 258 9.7 81 7 22
## 84 NA 295 11.5 82 7 23
## 85 80 294 8.6 86 7 24
## 86 108 223 8.0 85 7 25
## 87 20 81 8.6 82 7 26
## 88 52 82 12.0 86 7 27
## 89 82 213 7.4 88 7 28
## 90 50 275 7.4 86 7 29
## 91 64 253 7.4 83 7 30
## 92 59 254 9.2 81 7 31
## 93 39 83 6.9 81 8 1
## 94 9 24 13.8 81 8 2
## 95 16 77 7.4 82 8 3
## 96 78 NA 6.9 86 8 4
## 97 35 NA 7.4 85 8 5
## 98 66 NA 4.6 87 8 6
## 99 122 255 4.0 89 8 7
## 100 89 229 10.3 90 8 8
## 101 110 207 8.0 90 8 9
## 102 NA 222 8.6 92 8 10
## 103 NA 137 11.5 86 8 11
## 104 44 192 11.5 86 8 12
## 105 28 273 11.5 82 8 13
## 106 65 157 9.7 80 8 14
## 107 NA 64 11.5 79 8 15
## 108 22 71 10.3 77 8 16
## 109 59 51 6.3 79 8 17
## 110 23 115 7.4 76 8 18
## 111 31 244 10.9 78 8 19
## 112 44 190 10.3 78 8 20
## 113 21 259 15.5 77 8 21
## 114 9 36 14.3 72 8 22
## 115 NA 255 12.6 75 8 23
## 116 45 212 9.7 79 8 24
## 117 168 238 3.4 81 8 25
## 118 73 215 8.0 86 8 26
## 119 NA 153 5.7 88 8 27
## 120 76 203 9.7 97 8 28
## 121 118 225 2.3 94 8 29
## 122 84 237 6.3 96 8 30
## 123 85 188 6.3 94 8 31
## 124 96 167 6.9 91 9 1
## 125 78 197 5.1 92 9 2
## 126 73 183 2.8 93 9 3
## 127 91 189 4.6 93 9 4
## 128 47 95 7.4 87 9 5
## 129 32 92 15.5 84 9 6
## 130 20 252 10.9 80 9 7
## 131 23 220 10.3 78 9 8
## 132 21 230 10.9 75 9 9
## 133 24 259 9.7 73 9 10
## 134 44 236 14.9 81 9 11
## 135 21 259 15.5 76 9 12
## 136 28 238 6.3 77 9 13
## 137 9 24 10.9 71 9 14
## 138 13 112 11.5 71 9 15
## 139 46 237 6.9 78 9 16
## 140 18 224 13.8 67 9 17
## 141 13 27 10.3 76 9 18
## 142 24 238 10.3 68 9 19
## 143 16 201 8.0 82 9 20
## 144 13 238 12.6 64 9 21
## 145 23 14 9.2 71 9 22
## 146 36 139 10.3 81 9 23
## 147 7 49 10.3 69 9 24
## 148 14 20 16.6 63 9 25
## 149 30 193 6.9 70 9 26
## 150 NA 145 13.2 77 9 27
## 151 14 191 14.3 75 9 28
## 152 18 131 8.0 76 9 29
## 153 20 223 11.5 68 9 30
mean_ozone <- mean(airquality$Ozone, na.rm = TRUE)
median_ozone <- median(airquality$Ozone, na.rm = TRUE)
sd_ozone <- sd(airquality$Ozone, na.rm = TRUE)
cat("Mean Ozone:", mean_ozone, "\n")
## Mean Ozone: 42.12931
cat("Median Ozone:", median_ozone, "\n")
## Median Ozone: 31.5
cat("Standar Deviasi Ozone:", sd_ozone, "\n")
## Standar Deviasi Ozone: 32.98788
# b scaatter plot antara variabel wind dan temp
plot(airquality$Wind, airquality$Temp,
main = "Scatter Plot antara Wind dan Temp",
xlab = "Wind (mph)", ylab = "Temperature (F)",
pch = 19, col = "pink")
# 2 membuat bar chart untuk variabel cyl dri dataset mtcars
data("mtcars")
cyl_freq <- table(mtcars$cyl)
barplot(cyl_freq,
main = "Bar Chart Variabel Cyl",
xlab = "Jumlah Silinder", ylab = "Frekuensi",
col = "maroon")
text(x = barplot(cyl_freq, plot = FALSE),
y = cyl_freq,
labels = cyl_freq,
pos = 3, cex = 1.2, col = "pink")
# 3 a membuat boxplot
data("iris")
boxplot(Petal.Width ~ Species, data = iris,
main = "Boxplot Petal.Width Berdasarkan Species",
xlab = "Species", ylab = "Petal Width",
col = c("pink", "magenta", "maroon"))
#3 b Menghitung korelsi
correlation <- cor(iris$Sepal.Length, iris$Petal.Length)
cat("Korelasi antara Sepal.Length dan Petal.Length adalah:", correlation, "\n")
## Korelasi antara Sepal.Length dan Petal.Length adalah: 0.8717538
#Hasil korelasi 0.8717538 menunjukkan adanya korelasi positif yang kuat antara panjang sepal (sepal length) dan panjang petal (petal length).
# 3 c membuat sctter plot
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.3.3
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
geom_point(size = 3) +
geom_smooth(method = "lm", se = FALSE, linetype = "dashed") +
ggtitle("Scatter Plot Sepal.Length vs Sepal.Width") +
xlab("Sepal Length") +
ylab("Sepal Width") +
theme_minimal()
## `geom_smooth()` using formula = 'y ~ x'
# 4 Uji coba chisquare
tabel_chisq <- table(mtcars$vs, mtcars$am)
hasil_chisq <- chisq.test(tabel_chisq)
print(hasil_chisq)
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: tabel_chisq
## X-squared = 0.34754, df = 1, p-value = 0.5555
# 5a Menampilkan ringkasn model
model <- lm(Temp ~ Solar.R, data = airquality)
summary(model)
##
## Call:
## lm(formula = Temp ~ Solar.R, data = airquality)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.3787 -4.9572 0.8932 5.9111 18.4013
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 72.863012 1.693951 43.014 < 2e-16 ***
## Solar.R 0.028255 0.008205 3.444 0.000752 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.898 on 144 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.07609, Adjusted R-squared: 0.06967
## F-statistic: 11.86 on 1 and 144 DF, p-value: 0.0007518
# 5b Membuat sctter plot dengan garis regresi
plot(airquality$Solar.R, airquality$Temp,
main = "Scatter Plot dengan Garis Regresi",
xlab = "Solar.R", ylab = "Temperature",
pch = 19, col = "pink")
abline(model, col = "maroon", lwd = 2)
##5c Nilai p yang sangat kecil yaitu jauh di bawah 0.05 untuk kedua koefisien menunjukkan bahwa kedua variabel ini signifikan secara statistik. Artinya, pengaruh radiasi matahari terhadap suhu bukanlah kebetulan, tetapi memiliki hubungan yang signifikan.maka Dapat disimpulkan sebagai berikut:Ada hubungan positif antara radiasi matahari dan suhu. Semakin tinggi radiasi matahari, semakin tinggi pula suhu.