a) Is the spearman rank coefficient on the level 1 exam?

b) What properties does F-test have? Can’t find much on it online but Chi-square has a lot of properties like mean degrees of freedom and skew.

a) Is the spearman rank coefficient on the level 1 exam?

b) What properties does F-test have? Can’t find much on it online but Chi-square has a lot of properties like mean degrees of freedom and skew.

a) From the LOS: “distinguish between parametric and nonparametric tests and describe the situations in which the use of nonparametric tests may be appropriate” So you should definitely know what is it, when should it be used, interpret it and so on. But you are not required to calculate it b) there are two parameters: df1 and df2 - degrees of freedom of the samples

thank-you for your help. Do I need to understand what Degrees of Freedom really is? (other than N-1) I mean like the real math

Don’t need to know any of the ‘real math’.

^ Agreed, I got through all three levels without ever fully understanding degrees of freedom. The important thing is just to know what each of the tests stand for, when to use them and how to interpret the results. Carefully look at the learning outcome statements (LOS) at the start of each chapter. If the word calculate is used, you will want to learn basic calculations.

In reading 10 they explain the degrees of freedom. Dont know the page number and did not bring the book. But it gives a nice understanding why you use n-1 instead of n. It was described in the section where they introduce the t-distribution.

d.o.f. are just the number of things which are free to vary. Consider estimatation of std. dev., in that case you have a fixed (already estimated) mean that you are using, so if i was to tell you n-1 of the data points then you would already know the nth, since you know the mean/sum of the data points. So all but one of the observation is an independent bit of information so you have n-1 d.o.f. That’s just how i think of it, either that or n - number of parameters already estimated.