The number in the three different forms is as follows.
2/5 (fraction), 0.2 (decimal), and 20% (percent).
You could use percentages for the results of a poll. For example, if you held a poll for which flavour of ice cream is your favorite. Let's say you surveyed 100 kids and 50 kids voted chocolate, 30 vanilla, and 20 strawberry. Then you could say 20% voted for strawberry.
You could use the decimal form for money. For example, let's say you want to buy something that costs $10.20 and you only have 10 dollars. You could say that you would need 20 more cents, or $0.20.
As for the fraction, you could use this for measurements. For example, let's say you have a piece of fabric. You want to cut it up into 5 equal pieces, but only use two of those pieces. You could say that you would only use 2/5 of the fabric.
I hope I helped you out! If anything is wrong, please let me know about it! :)
Answer:
11.55
Step-by-step explanation:
SOH CAH TOA reminds you ...
Tan = Opposite/Adjacent
The angle at lower left is the complement of 60°, so is 30°. Then the side x satisfies the equation ...
tan(30°) = x/20
Multiplying by 20 gives ...
20·tan(30°) = x ≈ 11.55
f(x) = (x- zero) * (x -zero)
f(x) = (x--2) (x-8)
f(x) = (x+2) (x-8)
if you need it multiplied out
f(x) = x^2 +2x -8x-16
f(x) = x^2 -6x-16
Answer:
Below are the responses to the given question:
Step-by-step explanation:
Let X become the random marble variable & g have been any function.
Now.
For point a:
When X is discreet, the g(X) expectation is defined as follows
Then there will be a change of position.
E[g(X)] = X x∈X g(x)f(x)
If f is X and X's mass likelihood function support X.
For point b:
When X is continuing the g(X) expectations is calculated as, E[g(X)] = Z ∞ −∞ g(x)f(x) dx, where f is the X transportation distances of probability.If E(X) = −∞ or E(X) = ∞ (i.e., E(|X|) = ∞), they say it has nothing to expect from EX is occasionally written to stress that a specific probability distribution X is expected.Its expectation is given in the form of,E[g(X)] = Z x −∞ g(x) dF(x). , sometimes for the continuous random vary (x). Here F(x) is X's distributed feature. The anticipation operator bears the lineage of comprehensive & integral features. The superposition principle shows in detail how expectation maintains equality and is a skill.
