a <- c(2.7,4.6,2.6,3.0,3.2,3.8)
b <- c(4.6,3.4,2.9,3.5,4.1,5.1)
cor(a,b)
## [1] 0.104653
var(a)
## [1] 0.5776667
var(b)
## [1] 0.6746667
the cor 0.10 and var vara= 0.577 varb=0.674
if the mean of the two samples is the same, then the difference in the two means is zero. Null hypothesis.meanA-meanB=0. The alternate hypothesis is that the difference in means is not zero. meanA-meanB!=0
wilcox.test(a,b)
## Warning in wilcox.test.default(a, b): cannot compute exact p-value with ties
##
## Wilcoxon rank sum test with continuity correction
##
## data: a and b
## W = 9.5, p-value = 0.1994
## alternative hypothesis: true location shift is not equal to 0
p-value = 0.1994, alternative hypothesis: true location shift is not equal to 0. which means it does not affect the flow rate etch uniformaity
null hypothesis is H0 : µ1 = µ2
alternate hypothesis H1: µ1 != µ2
a <- c(2.7,4.6,2.6,3.0,3.2,3.8)
b <- c(4.6,3.4,2.9,3.5,4.1,5.1)
cor(a,b)
## [1] 0.104653
var(a)
## [1] 0.5776667
var(b)
## [1] 0.6746667
wilcox.test(a,b)
## Warning in wilcox.test.default(a, b): cannot compute exact p-value with ties
##
## Wilcoxon rank sum test with continuity correction
##
## data: a and b
## W = 9.5, p-value = 0.1994
## alternative hypothesis: true location shift is not equal to 0