We have seen that an orthogonal matrix with determinant 1 has at least one eigen- value = 1 , and an orthogonal matrix with determinant = − 1 has at least one eigen-value = − 1. Show that the other two eigenvalues in both cases are e i θ , e − i θ , which, of course, includes the real values 1 (when θ = 0 ), and − 1 (when θ = π ). Hint: See Problem 9, and remember that rotations and reflections do not change the length of vectors so eigenvalues must have absolute value = 1.
We have seen that an orthogonal matrix with determinant 1 has at least one eigen- value = 1 , and an orthogonal matrix with determinant = − 1 has at least one eigen-value = − 1. Show that the other two eigenvalues in both cases are e i θ , e − i θ , which, of course, includes the real values 1 (when θ = 0 ), and − 1 (when θ = π ). Hint: See Problem 9, and remember that rotations and reflections do not change the length of vectors so eigenvalues must have absolute value = 1.
We have seen that an orthogonal matrix with determinant 1 has at least one eigen- value
=
1
,
and an orthogonal matrix with determinant
=
−
1
has at least one eigen-value
=
−
1.
Show that the other two eigenvalues in both cases are
e
i
θ
,
e
−
i
θ
,
which, of course, includes the real values 1 (when
θ
=
0
), and
−
1
(when
θ
=
π
). Hint: See Problem 9, and remember that rotations and reflections do not change the length of vectors so eigenvalues must have absolute value
=
1.
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.
7. (a) Show that if A,, is an increasing sequence of measurable sets with limit A =
Un An, then P(A) is an increasing sequence converging to P(A).
(b) Repeat the same for a decreasing sequence.
(c) Show that the following inequalities hold:
P (lim inf An) lim inf P(A) ≤ lim sup P(A) ≤ P(lim sup A).
(d) Using the above inequalities, show that if A, A, then P(A) + P(A).
19. (a) Define the joint distribution and joint distribution function of a bivariate ran-
dom variable.
(b) Define its marginal distributions and marginal distribution functions.
(c) Explain how to compute the marginal distribution functions from the joint
distribution function.
18. Define a bivariate random variable. Provide an
example.
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