Let X 1 , X 2 , ... , X n be independent random variables having an unknown continuous distribution function F. and let Y 1 , Y 2 , ... , Y m be independent random variables having an unknown continuous distribution function G. Now order those n + m variables, and let I i = { 1 if the i th smallest of the n + m variables is from the X sample 0 otherwise The random variable R = ∑ i = 1 n + m i I i is the sum of the ranks of the X sample and is the basis of a standard statistical procedure (called theWilcoxon sum-of-ranks test) for testing whether F and G are identical distributions. This test accepts the hypothesis that F = G when R is neither too large nor too small. Assuming that the hypothesis of equality is in fact correct, compute the mean and variance of R. Hint: Use the results of Example 3e.
Let X 1 , X 2 , ... , X n be independent random variables having an unknown continuous distribution function F. and let Y 1 , Y 2 , ... , Y m be independent random variables having an unknown continuous distribution function G. Now order those n + m variables, and let I i = { 1 if the i th smallest of the n + m variables is from the X sample 0 otherwise The random variable R = ∑ i = 1 n + m i I i is the sum of the ranks of the X sample and is the basis of a standard statistical procedure (called theWilcoxon sum-of-ranks test) for testing whether F and G are identical distributions. This test accepts the hypothesis that F = G when R is neither too large nor too small. Assuming that the hypothesis of equality is in fact correct, compute the mean and variance of R. Hint: Use the results of Example 3e.
Let
X
1
,
X
2
,
...
,
X
n
be independent random variables having an unknown continuous distribution function F. and let
Y
1
,
Y
2
,
...
,
Y
m
be independent random variables having an unknown continuous distribution function G. Now order those
n
+
m
variables, and let
I
i
=
{
1
if the
i
th smallest of the
n
+
m
variables is from the
X
sample
0
otherwise
The random variable
R
=
∑
i
=
1
n
+
m
i
I
i
is the sum of the ranks of the X sample and is the basis of a standard statistical procedure (called theWilcoxon sum-of-ranks test) for testing whether F and G are identical distributions. This test accepts the hypothesis that
F
=
G
when R is neither too large nor too small. Assuming that the hypothesis of equality is in fact correct, compute the mean and variance of R.
Hint: Use the results of Example 3e.
Quantities that have magnitude and direction but not position. Some examples of vectors are velocity, displacement, acceleration, and force. They are sometimes called Euclidean or spatial vectors.
Question 3 [10 marks]. Suppose that X, Y and Z are statistically independent
random variables, each of them with a x²(2) distribution.
(a) Find the moment generating function of U = X + 3Y + Z. State clearly and
justify all steps taken.
(b) Calculate the expectation E(U) using the moment generating function.
Could you explain how to do part (c) please
Let X have a uniform distribution on (0,2) and let Y be independent of X with a uniform distribution over (0,3). Determine the cumulative distribution function of S=X+Y.
Please can you help me solve this question. Also, could you explain how you know at which intervals to split up the cases of the fucntion.
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