I'm assuming

(a) <em>f(x)</em> is a valid probability density function if its integral over the support is 1:

Compute the integral:

So we have
<em>k</em> / 6 = 1 → <em>k</em> = 6
(b) By definition of conditional probability,
P(<em>Y</em> ≤ 0.4 | <em>Y</em> ≤ 0.8) = P(<em>Y</em> ≤ 0.4 and <em>Y</em> ≤ 0.8) / P(<em>Y</em> ≤ 0.8)
P(<em>Y</em> ≤ 0.4 | <em>Y</em> ≤ 0.8) = P(<em>Y</em> ≤ 0.4) / P(<em>Y</em> ≤ 0.8)
It makes sense to derive the cumulative distribution function (CDF) for the rest of the problem, since <em>F(y)</em> = P(<em>Y</em> ≤ <em>y</em>).
We have

Then
P(<em>Y</em> ≤ 0.4) = <em>F</em> (0.4) = 0.352
P(<em>Y</em> ≤ 0.8) = <em>F</em> (0.8) = 0.896
and so
P(<em>Y</em> ≤ 0.4 | <em>Y</em> ≤ 0.8) = 0.352 / 0.896 ≈ 0.393
(c) The 0.95 quantile is the value <em>φ</em> such that
P(<em>Y</em> ≤ <em>φ</em>) = 0.95
In terms of the integral definition of the CDF, we have solve for <em>φ</em> such that

We have

which reduces to the cubic
3<em>φ</em>² - 2<em>φ</em>³ = 0.95
Use a calculator to solve this and find that <em>φ</em> ≈ 0.865.
Y=-2x+20
Input 50
Y=-2(50)+20
Y=-100+20
y=-80
I hope this helps :) feel free to ask if you need anything cleared up
Answer:
8463/32=264 15/32
Step-by-step explanation:
Let's make every fraction improper first.
7 3/4=31/4
5 2/8=42/8
6 1/2=13/2
Simplify 42/8
42/8=21/4
21/4
31/4
13/2=26/4
21/14*31/4*26/4=(21*31*26)/64=16926/64=8463/32
Answer:
11 : 22 : 132
Step-by-step explanation:
1 + 2 + 12 = 15
165 ÷ 15 = 11
1 × 11 = 11
2 × 11 = 22
12 × 11 = 132
11 : 22 : 132