Answer:
u = 4.604 , s = 2.903
u' = 23.025 , s' = 6.49
Step-by-step explanation:
Solution:
- We will use the distribution to calculate mean and standard deviation of random variable X.
- Mean = u = E ( X ) = Sum ( X*p(x) )
u = 1*0.229 + 2*0.113 + 3*0.114 + 4*0.076 + 5*0.052 + 6*0.027 + 7*0.031 + 8*0.358.
u = 4.604
- Standard deviation s = sqrt ( Var ( X ) = sqrt ( E ( X^2) + [E(X)]^2
s = sqrt [ 1*0.229 + 4*0.113 + 9*0.114 + 16*0.076 + 25*0.052 + 36*0.027 + 49*0.031 + 64*0.358 - 4.604^2 ]
s = sqrt ( 8.429184 )
s = 2.903
- We will use properties of E ( X ) and Var ( X ) as follows:
- Mean = u' = E (Rate*X) = Rate*E(X) = $5*u =
u' = $5*4.605
u' = 23.025
- standard deviation = s' = sqrt (Var (Rate*X) ) = sqrt(Rate)*sqrt(Var(X)) = sqrt($5)*s =
s' = sqrt($5)*2.903
u' = 6.49
the value of sin ∅ is 12/ 13
<h3>Quadrants and the "cast" Rule:</h3>
- In the first quadrant, the values for sin, cos, and tan are positive.
- In the second quadrant, the values for sin are positive only.
- In the third quadrant, the values for tan are positive only.
- In the fourth quadrant, the values for cos are positive only.
From the given question,
We have, cos ∅= 5/13
From the trigonometric identities, we have that
Then , let's substitute the value of cos ∅
Let make sin the subject of formula and find the squares of the fraction
sin²∅ =
Find the LCM
sin²∅ =
Find the difference
sin²∅ =
Find the square root
sin∅ =
sin∅ =
In quadrant II , sin is positive, so we have
sin ∅ = 12/ 13
Thus, the value of sin ∅ is 12/ 13
Learn more about quadrant here:
brainly.com/question/863849
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The probability of getting the same colour twice is approximately 34%.
ivied the total number of tennis balls buy the number of cans to see how many tennis balls are in 1 can
9 divided by 3 = 3
there are 3 tennis balls per can
fraction would be 3/1 ( meaning 3 balls to 1 can)
The equation will be of the form:
where A is the amount after t hours, and r is the decay constant.
To find the value of r, we plug the given values into the equation, giving:
Rearranging and taking natural logs of both sides, we get:
The required model is: