SHARE
TWEET

awsd

a guest Mar 26th, 2019 76 Never
Not a member of Pastebin yet? Sign Up, it unlocks many cool features!
  1. WEEK 6
  2. # SOAL 1
  3. data<-Nikkei
  4. data
  5. lowganjil <- numeric()
  6. highganjil <- numeric()
  7. closegenap <- numeric()
  8. opengenap <- numeric()
  9. a <- 1
  10. b <- 1
  11. for(i in 1:500){
  12.   if(i%%2==0){
  13.     closegenap[a] = Nikkei$Close[i]
  14.     opengenap[a] = Nikkei$Open[i]
  15.     a = a+1
  16.   }else{
  17.     lowganjil[b] = Nikkei$Low[i]
  18.     highganjil[b] = Nikkei$High[i]
  19.     b = b+1
  20.   }
  21. }
  22.   # 1.1
  23. t <- t.test(lowganjil, highganjil, sigma.x=sd(data$Low), sigma.y=sd(data$High))
  24. t
  25.  
  26.   # 1.2
  27. z <- z.test(closegenap, opengenap, sigma.x=sd(data$Close), sigma.y=sd(data$Open))
  28. z
  29.  
  30.  
  31.  
  32. # SOAL 2
  33.   # 2.1
  34. install.packages(mtcars)
  35. data2 <- mtcars
  36. View(mtcars)
  37.   # 2.3
  38. dratprima <- numeric()
  39. dratnonprima <- numeric()
  40. wtprima <- numeric()
  41. wtnonprima <- numeric()
  42. a <- 1
  43. b <- 1
  44.  
  45. for(i in 1:32){
  46.     if(i == 1){
  47.       dratnonprima[b] = data2$drat[i]
  48.       wtnonprima[b] = data2$wt[i]
  49.       b = b + 1
  50.     }else{
  51.       if( i %% 2 == 0 || i %% 3 == 0 || i %% 5 == 0 || i %% 7 == 0 ){
  52.         if(i == 2 || i == 3 || i == 5 || i == 7){
  53.           dratprima[a] = data2$drat[i]
  54.           wtprima[a] = data2$wt[i]
  55.           a = a + 1
  56.         }else{
  57.           dratnonprima[b] = data2$drat[i]
  58.           wtnonprima[b] = data2$wt[i]
  59.           b = b + 1
  60.         }
  61.       }
  62.       else{
  63.         dratprima[a] = data2$drat[i]
  64.         wtprima[a] = data2$wt[i]
  65.         a = a + 1
  66.       }
  67.     }
  68. }
  69.  
  70.   # 2.2
  71. dataframe21 <- data.frame(dratprima, wtprima)
  72. dataframe21
  73. dataframe22 <- data.frame(dratnonprima, wtnonprima)
  74. dataframe22
  75.  
  76.   # 2.4
  77. t <- t.test(dataframe21$dratprima, dataframe21$wtprima, sigma.x=sd(dataframe21$dratprima, sigma.y=sd(dataframe21$wtprima)))
  78. t
  79.  
  80. z <- z.test(dataframe22$dratnonprima, dataframe22$wtnonprima, sigma.x=sd(dataframe22$dratnonprima), sigma.y=sd(dataframe22$wtnonprima))
  81. z
  82.  
  83. # SOAL 3
  84. data3 <- airquality
  85. View(airquality)
  86. a <- 1
  87. b <- 1
  88. ozone <- numeric()
  89. solar <- numeric()
  90. wind <- numeric()
  91. temp <- numeric()
  92. for(i in 1:153){
  93.   if(i<=50){
  94.     ozone[a]=data3$Ozone[i]
  95.     solar[a]=data3$Solar.R[i]
  96.     a = a+1
  97.   } else if(i>=104){
  98.     wind[b]=data3$Wind[i]
  99.     temp[b]=data3$Temp[i]
  100.     b=b+1
  101.   }else{}
  102. }
  103. t.test(ozone, solar, sigma.x=sd(data3$Ozone), sigma.y=sd(data3$Solar.R))
  104.  
  105. z.test(wind, temp, sigma.x=sd(data3$Wind), sigma.y=sd(data3$Temp))
  106.  
  107. # SOAL 4
  108. set.seed(500)
  109. datarandom <- data.frame(replicate(1, sample(1:100, 500, rep = TRUE)))
  110. datarandom
  111. data4 <- datarandom[1:250, 1]
  112. data4
  113. data5 <- datarandom[251:500, 1]
  114. data5
  115. t.test(data4, data5, sigma.x=sd(data4), sigma.y=sd(data5))
  116. z.test(data4, data5, sigma.x=sd(data4), sigma.y=sd(data5))
  117.  
  118. # SOAL 5
  119. data6 <- 100
  120. fibo <- numeric(data6)
  121. fibo[1] <- 1
  122. fibo[2] <- 1
  123. for(i in 3:data6){
  124.   fibo[i] <- fibo[i-1] + fibo[i-2]
  125. }
  126. set.seed(100)
  127. datarandom2 <- data.frame(replicate(1, sample(1:100, 100, rep = TRUE)))
  128. datarandom2
  129. data7 <- data.frame(fibo, datarandom2)
  130. data7
  131. data8 <- data7[1:50, 1:2]
  132. colnames(data8) <- c("fibonacci", "random")
  133. data8
  134. data9 <- data7[51:100, 1:2]
  135. colnames(data9) <- c("fibonacci", "random")
  136. data9
  137.  
  138. t.test(data8$fibonacci, data8$random, sigma.x=sd(data8$fibonacci), sigma.y=sd(data8$random))
  139. z.test(data8$fibonacci, data8$random, sigma.x=sd(data8$fibonacci), sigma.y=sd(data8$random))
  140. t.test(data9$fibonacci, data9$random, sigma.x=sd(data9$fibonacci), sigma.y=sd(data9$random))
  141. z.test(data9$fibonacci, data9$random, sigma.x=sd(data9$fibonacci), sigma.y=sd(data9$random))
  142.  
  143.  
  144.  
  145.  
  146.  
  147.  
  148.  
  149.  
  150.  
  151.  
  152. WEEK 5
  153. a <- c(70,85,76,90,85,60,93,80)
  154.  
  155. View(rivers)
  156. data <- rivers
  157.  
  158.  
  159.  
  160. #T-Test no. 1
  161. hasil <- (((mean(data)-600)*sqrt(141)/sd(data)))
  162. hasil
  163.  
  164. View(Orange)
  165. data1 <- Orange
  166.  
  167. mean(Orange$circumference)
  168.  
  169.  
  170.  
  171. #Z-Test no. 2
  172. z <- ((mean(data1$circumference)-116)/(sd(data1$circumference)/sqrt(35)))
  173. z
  174.  
  175. #Ho = hipotesa awal = <10.000
  176. #Ha = hipotesa alternatif (ekspektasi) = >10.000
  177.  
  178. #Jika Alpha > Z-Test, maka Ha ditolak Ho diterima
  179. #kesimpulan: Perusahaan dapat memakai bola lampu lebih dari 10.000 jam
  180.  
  181. #Jika Alpha < Z-Test, maka Ho ditolak Ha diterima
  182. #kesimpulan: Perusahaan dapat memakai bola lampu kurang dari 10.000 jam
  183.  
  184.  
  185.  
  186. #No. 3
  187. alpha <- qnorm(1-.05)
  188. alpha
  189. -alpha
  190.  
  191. ztest <- (9900-10000)/(120/30)
  192. ztest
  193.  
  194. ztest <- (9900-10000)/(120/sqrt(30))
  195. ztest
  196.  
  197. a = -z
  198. a
  199.  
  200. ztest
  201. #Jadi Ha ditolak, dan Ho diterima
  202.  
  203.  
  204.  
  205. #No. 4a
  206. b <- c(170,156,183,156,187,167,163,179,187,167,174,156,
  207.        174,179,159,179,187,179,183,170,179,156,170,174,179)
  208. mean4 <- mean(b)
  209. mean4
  210.  
  211. #T-Test
  212. t4 <- ((mean(b)-170)*(sqrt(25)/sd(b)))
  213. t4
  214.  
  215. #Z-Test
  216. z4 <- ((mean(b)-170)-(sd(b)/sqrt(25)))
  217. z4
  218.  
  219. pval <- 2*pt(-abs(t4), df = 25-1)
  220. pval
  221.  
  222. #No. 4b
  223. #tvalue = 1.71
  224. #kesimpulan Ha diterima dan Ho ditolak
  225.  
  226.  
  227.  
  228. #no. 5
  229. q <- qnorm(1-.05)
  230. a
  231. -a
  232.  
  233. x <- ((2.1-2)/(0.25/sqrt(35)))
  234. pval1 <- pnorm(x)
  235. pval1
  236. x.alpha <- qnorm(1-.05)
  237. x.alpha
  238.  
  239. w = -z
  240. w
  241. #Alpha < Z, maka Ha diterima, Ho ditolak
  242.  
  243.  
  244.  
  245.  
  246.  
  247.  
  248.  
  249.  
  250.  
  251.  
  252.  
  253.  
  254. WEEK 4
  255. mean1 <- mean(FilePendukung$Umur)
  256.  
  257. med1 <-median(FilePendukung$Umur)
  258.  
  259. modus1 <- getmode(FilePendukung$Umur)
  260. getmode <- function(v) {
  261.   uniqv <- unique(v)
  262.   uniqv[which.max(tabulate(match(v, uniqv)))]
  263. }
  264.  
  265.  
  266. hasil <- getmode(FilePendukung$Umur)
  267. hasil
  268.  
  269. max1 <- max(FilePendukung$Umur)
  270.  
  271. min1 <- min(FilePendukung$Umur)
  272.  
  273. range1 <- range(FilePendukung$Umur)
  274.  
  275. var1 <- var(FilePendukung$Umur)
  276.  
  277. sd1 <- sd(FilePendukung$Umur)
  278.  
  279. quan1 <- quantile(FilePendukung$Umur)
  280.  
  281. DataBar1 <- c(mean1, med1, modus1, max1, min1, range1, var1, sd1, quan1)
  282. barplot(DataBar1, main = "Barplot Kolom Umur", col = c("tomato", "sienna1", "yellow2", "olivedrab2", "deepskyblue1"))
  283.  
  284.  
  285.  
  286.  
  287.  
  288. mean2 <- mean(FilePendukung$Suku)
  289. mean3 <- mean(FilePendukung$Pendidikan)
  290.  
  291. med2 <- median(FilePendukung$Suku)
  292. med3 <- median(FilePendukung$Pendidikan)
  293.  
  294. modus2 <- getmode(FilePendukung$Suku)
  295. getmode <- function(v) {
  296.   uniqv <- unique(v)
  297.   uniqv[which.max(tabulate(match(v, uniqv)))]
  298. }
  299.  
  300.  
  301. hasill <- getmode(FilePendukung$Suku)
  302. hasill
  303. modus3 <- getmode(FilePendukung$Pendidikan)
  304. getmode <- function(v) {
  305.   uniqv <- unique(v)
  306.   uniqv[which.max(tabulate(match(v, uniqv)))]
  307. }
  308.  
  309.  
  310. hasilll <- getmode(FilePendukung$Pendidikan)
  311. hasilll
  312.  
  313.  
  314. DataBar2 <- c(mean2, med2, modus2)
  315. DataBar3 <- c(mean3, med3, modus3)
  316. barplot(DataBar2, main = "Barplot Kolom Suku", col = c("orangered", "orange", "yellow2", "olivedrab3"))
  317. barplot(DataBar3, main = "Barplot Kolom Pendidikan", col = c("gold", "goldenrod1", "goldenrod"))
  318.  
  319.  
  320.  
  321.  
  322.  
  323.  
  324. a <- quantile(FilePendukung$JenisKelamin, c(.61))
  325. b <- quantile(FilePendukung$Status, c(.61))
  326. c <- quantile(FilePendukung$StatusRumah, c(.61))
  327. d <- quantile(FilePendukung$Suku, c(.61))
  328. e <- quantile(FilePendukung$Pendidikan, c(.61))
  329. f <- quantile(FilePendukung$Pekerjaan, c(.61))
  330. g <- quantile(FilePendukung$Umur, c(.61))
  331. h <- quantile(FilePendukung$PengeluaranPerBulan, c(.61))
  332. i <- quantile(FilePendukung$Minat, c(.61))
  333.  
  334. DataBar4 <- c(a, b, c, d, e, f, g, h, i)
  335. plot(DataBar4, pch = c(1:9), main = "Plot Lines File Pendukung")
  336. lines(DataBar4, col = "darkslateblue")
RAW Paste Data
We use cookies for various purposes including analytics. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy. OK, I Understand
 
Top