By Ashley Briscoe R Assignment 3

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", var.equal=FALSE)

    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

Yes. We can conclude that individuals of German nationality are typically taller than individuals of Italian nationality.

x = c(5260,5470,5640,6180,6390,6515,6805,7515,7515,8230,8770) 
t.test(x, alternative="two.sided", mu=7725)

    One Sample t-test

data:  x
t = -2.8208, df = 10, p-value = 0.01814
alternative hypothesis: true mean is not equal to 7725
95 percent confidence interval:
 5986.348 7520.925
sample estimates:
mean of x 
 6753.636

Yes. The women’s energy intake does deviate systematically from the recommended intake.

Before = 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(Before,After,alternative="less", var.equal=TRUE)

    Two Sample t-test

data:  Before and After
t = -0.043323, df = 18, p-value = 0.483
alternative hypothesis: true difference in means is less than 0
95 percent confidence interval:
     -Inf 1.951298
sample estimates:
mean of x mean of y 
    14.48     14.53

No. The new type of training is not effective.

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