it <- c(175,168,168,190,156,181,182,175,174,179)
ger <- c(185,169,173,173,188,186,175,174,179,180)
t.test(it,ger,alternative = "less")
Welch Two Sample t-test
data: it and ger
t = -0.94737, df = 15.981, p-value = 0.1788
alternative hypothesis: true difference in means is less than 0
95 percent confidence interval:
-Inf 2.866204
sample estimates:
mean of x mean of y
174.8 178.2 Fail to reject the null hypothesis.
intake <- c(5260,5470,5640,6180,6390,6515,6805,7515,7515,8230,8770)
t.test(intake,alternative = "greater",mu=7725)
One Sample t-test
data: intake
t = -2.8208, df = 10, p-value = 0.9909
alternative hypothesis: true mean is greater than 7725
95 percent confidence interval:
6129.492 Inf
sample estimates:
mean of x
6753.636 Yes, it is greater than mean value.
bef <- c(12.9,13.5,12.8,15.6,17.2,19.2,12.6,15.3,14.4,11.3)
after <- c(12.7,13.6,12.0,15.2,16.8,20.0,12.0,15.9,16.0,11.1)
t.test(after,bef,alternative = "greater",paired = TRUE)
Paired t-test
data: after and bef
t = 0.21331, df = 9, p-value = 0.4179
alternative hypothesis: true difference in means is greater than 0
95 percent confidence interval:
-0.3796859 Inf
sample estimates:
mean of the differences
0.05 The new type of training shows to be more effective.