Since g(x) varies with x, therefore:
g(x) = k/x where k is a constant.
So, first we need to get k. We are given that g(x) = 0.2 when x = 0.1
Substitute with these values to get k as follows:
g(x) = k/x
0.2 = k/0.1
k = 0.2*0.1 = 0.02
Now, the equation became:
g(x) = 0.02 / x
We need to get the g(x) when x = 1.6
Therefore, we will substitute with x in the equation and calculate the corresponding g as follows:
g(x) = 0.02 / 1.6
g(x) = 0.0125
Answer:
(3x + 5)/2 = 7
Step-by-step explanation:
If you substitute 3 for x, you get the equation:
(3(3) + 5)/2 = 7
(9 + 5)/2 = 7
14/2 = 7
Answer: 19
Step-by-step explanation:
Multiplying both sides of the equation by 
gives that 
.
Looks like a badly encoded/decoded symbol. It's supposed to be a minus sign, so you're asked to find the expectation of 2<em>X </em>² - <em>Y</em>.
If you don't know how <em>X</em> or <em>Y</em> are distributed, but you know E[<em>X</em> ²] and E[<em>Y</em>], then it's as simple as distributing the expectation over the sum:
E[2<em>X </em>² - <em>Y</em>] = 2 E[<em>X </em>²] - E[<em>Y</em>]
Or, if you're given the expectation and variance of <em>X</em>, you have
Var[<em>X</em>] = E[<em>X</em> ²] - E[<em>X</em>]²
→ E[2<em>X </em>² - <em>Y</em>] = 2 (Var[<em>X</em>] + E[<em>X</em>]²) - E[<em>Y</em>]
Otherwise, you may be given the density function, or joint density, in which case you can determine the expectations by computing an integral or sum.
