Let X 1 and X 2 be independent normal random variables, each having mean 10 and variance σ 2 . Which probability is larger a. P ( X 1 > 15 ) or P ( X 1 + X 2 > 25 ) ; b. P ( X 1 > 15 ) or P ( X 1 + X 2 > 30 ) .
Let X 1 and X 2 be independent normal random variables, each having mean 10 and variance σ 2 . Which probability is larger a. P ( X 1 > 15 ) or P ( X 1 + X 2 > 25 ) ; b. P ( X 1 > 15 ) or P ( X 1 + X 2 > 30 ) .
Solution Summary: The author explains that if the value shifts towards mean, then the area above it also increases.
Let
X
1
and
X
2
be independent normal random variables, each having mean 10 and variance
σ
2
. Which probability is larger
a.
P
(
X
1
>
15
)
or
P
(
X
1
+
X
2
>
25
)
;
b.
P
(
X
1
>
15
)
or
P
(
X
1
+
X
2
>
30
)
.
Definition Definition Measure of central tendency that is the average of a given data set. The mean value is evaluated as the quotient of the sum of all observations by the sample size. The mean, in contrast to a median, is affected by extreme values. Very large or very small values can distract the mean from the center of the data. Arithmetic mean: The most common type of mean is the arithmetic mean. It is evaluated using the formula: μ = 1 N ∑ i = 1 N x i Other types of means are the geometric mean, logarithmic mean, and harmonic mean. Geometric mean: The nth root of the product of n observations from a data set is defined as the geometric mean of the set: G = x 1 x 2 ... x n n Logarithmic mean: The difference of the natural logarithms of the two numbers, divided by the difference between the numbers is the logarithmic mean of the two numbers. The logarithmic mean is used particularly in heat transfer and mass transfer. ln x 2 − ln x 1 x 2 − x 1 Harmonic mean: The inverse of the arithmetic mean of the inverses of all the numbers in a data set is the harmonic mean of the data. 1 1 x 1 + 1 x 2 + ...
Question 1: Let X be a random variable with p.m.f
(|x| +1)²
x= -2, -1, 0, 1,2
f(x) =
C
0,
O.W
1. The value of c.
2. The c.d.f.
3. E(X).
4. E(2x+3).
5. E(X²).
6. E(3x²+4).
7. E(X(3X+4)).
8. Var(X).
9. Var (6-3X).
10. Find the m.g.f of the random variable X
Please could you explain how to do integration by parts for this question in detail please
2. Claim events on a portfolio of insurance policies follow a Poisson process with parameter
A. Individual claim amounts follow a distribution X with density:
f(x)=0.0122re001, g>0.
The insurance company calculates premiums using a premium loading of 45%.
(a) Derive the moment generating function Mx(t).
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, probability and related others by exploring similar questions and additional content below.
Discrete Distributions: Binomial, Poisson and Hypergeometric | Statistics for Data Science; Author: Dr. Bharatendra Rai;https://www.youtube.com/watch?v=lHhyy4JMigg;License: Standard Youtube License