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

>