输入

注释

> 

expend <- c(rnorm(10))

随机产生10个正态分布的数据

> 

stature<- c("obese","lean","obese","lean","obese","lean","obese","lean","obese","lean")

> 

d <- data.frame(expend,stature)

数据框赋值

> 

t.test(expend~stature)

T检验

结果

Welch Two Sample t-test

data:  expend by stature

t = -9e-04, df = 7.985, p-value = 0.9993

alternative hypothesis: true difference in means is not equal to 0

95 percent confidence interval:

 -1.204426  1.203442

sample estimates:

 mean in group lean mean in group obese

         0.04850100          0.04899317

不显著

因为我们是交替给两组数据赋值，当然不显著

输入

注释

> 

rm(stature, d)

清空stature和d的原值

> 

stature<- c("lean","lean","lean","lean","lean","obese","obese","obese","obese","obese")

为stature进行区间赋值

> 

d <- data.frame(expend,stature)

数据框赋值

> 

t.test(expend~stature)

T检验

结果

        Welch Two Sample t-test

data:  expend by stature

t = -1.4237, df = 5.974, p-value = 0.2046

alternative hypothesis: true difference in means is not equal to 0

95 percent confidence interval:

 -1.8056111  0.4781883

sample estimates:

 mean in group lean mean in group obese

         -0.2831086           0.3806028

不显著

区间赋值属于有规律的赋值，显著性提高

输入

> 

data<-read.csv("D:\ziliao\zhuanye\R bear\vocal cords.csv")

读入表格

> 

grah <- data[,1]

第一列值赋给grah

> 

torm <- data[,2]

第二列值赋给torm

> 

d <- data.frame(grah, torm)

数据框赋值

> 

t.test(grah, torm)

T检验

结果

        Welch Two Sample t-test

data:  grah and torm

t = 6.4396, df = 11.45, p-value = 3.995e-05

alternative hypothesis: true difference in means is not equal to 0

95 percent confidence interval:

  67.89471 137.89651

sample estimates:

mean of x mean of y

174.93756  72.04194

显著