This is a school project. The statistical analysis is simply an attempt to show that some thought went into the sample size and it was not chosen randomly.
I did some research and one study suggested taking a couple samples, finding the minimum number of samples for a z-distribution, then use that value of n in determining the degrees of freedom for the t-distribution. Then I solve for the new n. with each new sample everything is recalulated, S, n...
That is exacting what I am trying to say. If "frequentist" statistics are not the right route what theories/methods should I be looking at?
I am trying to find the coefficient of friction, and I plan to use the sample mean as the estimator.
This is not a homework problem.
I am working on an experiment and I need to know how many samples (n) I need to achieve a margin of error (e) below 2%.
Looking through a statistics textbook they provide a calculation for e using z-distributions, but not t-distributions.
Replacing...
In a solution to an old exam the prof found a basis of a 5x5 matrix by simply subtracting the transpose of A from A aka(A - A^T) and then found the basis for the null for the now much simplified matrix.
Is that a general guideline I can follow? If so it would make my life a lot easier.
I think you are missing the point of what mechanical thinking means in this context. By mechanical thinking the author assumes that the reader can understand the theorems as they stand with a level of clarity that they can then turn around and use them creatively on real problems.
What...
"Yeah, it as if they expected us to think!"
By "think" I can only assume you are referring to mechanical thinking. Believe me, I know exactly why the book is written the way it is and why it is such a flawed approach. The author, like most mechanical thinkers, over assumes and is incapable of...
Find a basis for and calculate the dimension of nullA:
A = [1 2 7]T, [1 1 2]T, [-2 0 6]T, [0 1 -10]T, [4 -5 -7]T
Like most algebra texts mine has pages and pages of proofs with hardly a single example tying it together.
Here is what I think I know:
If the determinant does not equal 0...
Yeah, I am starting to see that.
I also found another theorem which counters what I posted before.
An nxn matrix is invertible if and only if
1) the rows are linearly independent (otherwise det = 0)*
2) the columns are linearly independent (otherwise det = 0)*
3) rows of A span R^n
So in...
Ok, to clear things up a little
Given vectors X1, X2..., X3 in R^n, a vector in the form X = t1 X1 + t2 X2 + ... tk Xk
1. the set of such linear combinations is called a span of the Xi and denoted by span{X1, X2, ...Xk}
2. If a family of vectors is linearly independent none of them can be...
I think I have something mixed up so if someone can please point out my error.
1. the set of all linear combinations is called a span.
2. If a family of vectors is linearly independent none of them can be written as a linear combination of finitely many other vectors in the collection.
3. If...
Ok, I figured it out. Because of the direction of the vector it is not 0 degrees + 48.6 degrees but 180 degrees - 48.6 degrees which equals 131.4 degrees
I am working on a statics question and one of the angles is sin^-1(0.750) which I calculate as 48.6 degrees though the book gives 131.41 degrees
If I calculate sin(48.6) it equals 0.750
If I calculate sin(131.41) it equals 0.750
Clearly there is some relation or rule that I am unaware of...