讀入檔案
把年份轉成類別變數(1=2013,2=2014…..)
Ta <- read.csv("ANOVAT.csv")
Ta$Year <- as.factor(Ta$Year)
年分 vs 駁油量
result2 <- aov(Ta$KL ~ Ta$Year)
summary(result2) #無顯著差異
## Df Sum Sq Mean Sq F value Pr(>F)
## Ta$Year 5 571445 114289 0.822 0.535
## Residuals 186 25863097 139049
TukeyHSD(result2)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Ta$KL ~ Ta$Year)
##
## $`Ta$Year`
## diff lwr upr p adj
## 2-1 2.582961 -219.1270 224.2929 1.0000000
## 3-1 -50.794794 -281.1798 179.5902 0.9882478
## 4-1 94.128323 -185.2195 373.4762 0.9267727
## 5-1 173.936278 -238.0779 585.9505 0.8287804
## 6-1 50.185701 -214.3363 314.7077 0.9941238
## 3-2 -53.377755 -279.1775 172.4220 0.9838921
## 4-2 91.545362 -184.0331 367.1238 0.9308429
## 5-2 171.353316 -238.1146 580.8213 0.8339723
## 6-2 47.602739 -212.9355 308.1410 0.9950733
## 4-3 144.923117 -137.6817 427.5279 0.6795283
## 5-3 224.731071 -189.4983 638.9604 0.6243601
## 6-3 100.980495 -166.9787 368.9397 0.8867918
## 5-4 79.807955 -363.5248 523.1407 0.9954048
## 6-4 -43.942622 -355.0055 267.1202 0.9985510
## 6-5 -123.750577 -557.8941 310.3929 0.9633907
# 2-1 表示第二年和第一年的比較
# diff 表示兩年之間的駁油量差距
# P adj 看有沒有顯著
駁油量 VS 運費
Tb <- read.csv("T.csv")
colnames(Tb) <- c("Date","Spinfo","KL","Bill","trans_fee","TotalP","O_price")
Tb$trans_fee <- as.factor(Tb$trans_fee)
把運費轉成類別變數
result1 <- aov(Tb$KL ~ Tb$trans_fee)
summary(result1)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tb$trans_fee 4 129690 32423 0.23 0.921
## Residuals 187 26304851 140668
TukeyHSD(result1)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Tb$KL ~ Tb$trans_fee)
##
## $`Tb$trans_fee`
## diff lwr upr p adj
## 188-168 -65.431372 -402.4422 271.5795 0.9836293
## 407-168 -69.820250 -559.8736 420.2331 0.9949500
## 414-168 -37.311500 -527.3649 452.7419 0.9995662
## 489-168 33.518000 -428.5088 495.5448 0.9996417
## 407-188 -4.388878 -378.9018 370.1240 0.9999998
## 414-188 28.119872 -346.3930 402.6328 0.9995894
## 489-188 98.949372 -238.0615 435.9602 0.9276815
## 414-407 32.508750 -484.0529 549.0704 0.9997961
## 489-407 103.338250 -386.7151 593.3916 0.9777567
## 489-414 70.829500 -419.2239 560.8829 0.9946629
不同的運費(類別) VS 石油價格 是否具有顯著差異
result3 <- aov(Tb$O_price ~ Tb$trans_fee)
summary(result3)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tb$trans_fee 4 3.259e+08 81480639 3.942 0.00427 **
## Residuals 187 3.865e+09 20670709
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(result3)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Tb$O_price ~ Tb$trans_fee)
##
## $`Tb$trans_fee`
## diff lwr upr p adj
## 188-168 -4779.3308 -8864.635 -694.0261 0.0128777
## 407-168 -8144.1000 -14084.613 -2203.5868 0.0019749
## 414-168 -5769.1000 -11709.613 171.4132 0.0615573
## 489-168 -4669.1000 -10269.870 931.6696 0.1506418
## 407-188 -3364.7692 -7904.680 1175.1418 0.2504251
## 414-188 -989.7692 -5529.680 3550.1418 0.9748581
## 489-188 110.2308 -3975.074 4195.5355 0.9999930
## 414-407 2375.0000 -3886.851 8636.8508 0.8341315
## 489-407 3475.0000 -2465.513 9415.5132 0.4920353
## 489-414 1100.0000 -4840.513 7040.5132 0.9862909
我們可以得知不同的運費下的石油價格具有顯著差異，其中運費168對於運費188和407具有顯著差異。