數學成績 <- c(85,91,74,100,82,84,78,100,51,70)
統計成績 <- c(68,85,74,88,63,78,90,80,58,63)
plot(數學成績,統計成績,
pch = 17,
col= "skyblue",
main ="統計成績與數學成績",
xlab ="數學成績",
ylab ="統計成績 ")
abc <- c(68,85,74,88,63,78,90,80,58,63)
hist(abc,
col= "lightyellow",
main ="統計成績",
xlab ="statistic",
ylab ="")
data<- c(185,82,36,28,25)
labels <- c("休閒娛樂","知識閱讀","體育競技","科學創新","公益活動")
pie(data,labels,main ="參加社團比率", col=terrain.colors(length(data)))
Data <- read.csv("C:/1.csv")
head(Data)
## Name Statistic Math Japanese Management Accounting
## 1 張青松 68 85 84 89 86
## 2 王奕翔 85 91 63 76 66
## 3 田新雨 74 74 61 80 69
## 4 徐麗娜 88 100 49 71 66
## 5 張志傑 63 82 89 78 80
## 6 趙穎睿 78 84 51 60 60
## Name Statistic Math Japanese Management Accounting
## 1 張青松 68 85 84 89 86
## 2 王奕翔 85 91 63 76 66
## 3 田新雨 74 74 61 80 69
## 4 徐麗娜 88 100 49 71 66
## 5 張志傑 63 82 89 78 80
## 6 趙穎睿 78 84 51 60 60
print(Data)
## Name Statistic Math Japanese Management Accounting
## 1 張青松 68 85 84 89 86
## 2 王奕翔 85 91 63 76 66
## 3 田新雨 74 74 61 80 69
## 4 徐麗娜 88 100 49 71 66
## 5 張志傑 63 82 89 78 80
## 6 趙穎睿 78 84 51 60 60
## 7 王智強 90 78 59 72 66
## 8 宋媛婷 80 100 53 73 70
## 9 袁四方 58 51 79 91 85
## 10 張建國 63 70 91 85 82
## Name Statistic Math Japanese Management Accounting
## 1 張青松 68 85 84 89 86
## 2 王奕翔 85 91 63 76 66
## 3 田新雨 74 74 61 80 69
## 4 徐麗娜 88 100 49 71 66
## 5 張志傑 63 82 89 78 80
## 6 趙穎睿 78 84 51 60 60
## 7 王智強 90 78 59 72 66
## 8 宋媛婷 80 100 53 73 70
## 9 袁四方 58 51 79 91 85
## 10 張建國 63 70 91 85 82
summary(Data)
## Name Statistic Math Japanese
## Length:10 Min. :58.00 Min. : 51.0 Min. :49.00
## Class :character 1st Qu.:64.25 1st Qu.: 75.0 1st Qu.:54.50
## Mode :character Median :76.00 Median : 83.0 Median :62.00
## Mean :74.70 Mean : 81.5 Mean :67.90
## 3rd Qu.:83.75 3rd Qu.: 89.5 3rd Qu.:82.75
## Max. :90.00 Max. :100.0 Max. :91.00
## Management Accounting
## Min. :60.00 Min. :60.0
## 1st Qu.:72.25 1st Qu.:66.0
## Median :77.00 Median :69.5
## Mean :77.50 Mean :73.0
## 3rd Qu.:83.75 3rd Qu.:81.5
## Max. :91.00 Max. :86.0
## Name Statistic Math Japanese
## Length:10 Min. :58.00 Min. : 51.0 Min. :49.00
## Class :character 1st Qu.:64.25 1st Qu.: 75.0 1st Qu.:54.50
## Mode :character Median :76.00 Median : 83.0 Median :62.00
## Mean :74.70 Mean : 81.5 Mean :67.90
## 3rd Qu.:83.75 3rd Qu.: 89.5 3rd Qu.:82.75
## Max. :90.00 Max. :100.0 Max. :91.00
## Management Accounting
## Min. :60.00 Min. :60.0
## 1st Qu.:72.25 1st Qu.:66.0
## Median :77.00 Median :69.5
## Mean :77.50 Mean :73.0
## 3rd Qu.:83.75 3rd Qu.:81.5
## Max. :91.00 Max. :86.0
stem(Data$Japanese)
##
## The decimal point is 1 digit(s) to the right of the |
##
## 4 | 9
## 5 | 139
## 6 | 13
## 7 | 9
## 8 | 49
## 9 | 1
##
## The decimal point is 1 digit(s) to the right of the |
##
## 4 | 9
## 5 | 139
## 6 | 13
## 7 | 9
## 8 | 49
## 9 | 1
mean(Data$Japanese) #平均數
## [1] 67.9
## [1] 67.9
median(Data$Japanese) #中位數
## [1] 62
## [1] 62
as.numeric(names(table(Data$Japanese)))[which.max(table(Data$Japanese))] #眾數
## [1] 49
## [1] 49
sd(Data$Japanese) #standard deviation
## [1] 16.25115
## [1] 16.25115
var(Data$Japanese) #variance
## [1] 264.1
## [1] 264.1
Q1 <- quantile(Data$Japanese, 1 / 4)
Q3 <- quantile(Data$Japanese, 3 / 4)