a = c(175, 168, 168, 190, 156, 181, 182, 175, 174, 179)
b = c(185, 169, 173, 173, 188, 186, 175, 174, 179, 180)
x<-t.test(a,b)
str(x)
class(x)
> str(x)
List of 9
$ statistic : Named num -0.947
..- attr(*, "names")= chr "t"
$ parameter : Named num 16
..- attr(*, "names")= chr "df"
$ p.value : num 0.358
$ conf.int : num [1:2] -11.01 4.21
..- attr(*, "conf.level")= num 0.95
$ estimate : Named num [1:2] 175 178
..- attr(*, "names")= chr [1:2] "mean of x" "mean of y"
$ null.value : Named num 0
..- attr(*, "names")= chr "difference in means"
$ alternative: chr "two.sided"
$ method : chr "Welch Two Sample t-test"
$ data.name : chr "a and b"
- attr(*, "class")= chr "htest"
> class(x)
[1] "htest"
> x$p.value
[1] 0.3575549
> x$estimate
mean of x mean of y
174.8 178.2
> x$estimate[1]
mean of x
174.8
> x$estimate[2]
mean of y
178.2
>
========================
> x<-cor.test(a,b)
> str(x)
List of 9
$ statistic : Named num -0.714
..- attr(*, "names")= chr "t"
$ parameter : Named int 8
..- attr(*, "names")= chr "df"
$ p.value : num 0.496
$ estimate : Named num -0.245
..- attr(*, "names")= chr "cor"
$ null.value : Named num 0
..- attr(*, "names")= chr "correlation"
$ alternative: chr "two.sided"
$ method : chr "Pearson's product-moment correlation"
$ data.name : chr "a and b"
$ conf.int : num [1:2] -0.758 0.455
..- attr(*, "conf.level")= num 0.95
- attr(*, "class")= chr "htest"
> x$p.value
[1] 0.4955273
> x$estimate
cor
-0.2447594
>