For the answer to the question above asking w<span>hy do we need to integrate probabilities in statistics?
Well, i</span>ntegration is used very very often in theoretical statistics. Transformation relates to the result that data values that are modelled as being random variables from any given continuous distribution can be converted to random variables having a standard uniform distribution. So, we need to see other possibilities by combining<span> (one thing) with another.</span>
Answer:
3x+10 = 2x+40
3x - 2x = +40 - 10
x = 30
Step-by-step explanation:
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Because the random variable x follows a continuous uniform distribution from x=1 to x=5, therefore
p(x) = 1/4, x=[1, 5]
The value of p(x) ensures that the total area under the curve = 1.
The conditional probability p(x > 2.5 | x ≤ 4) is the shaded portion of the curve. Its value is
p(x > 2.5 | x ≤4) = (1/4)*(4 - 2.5) = 0.375
Answer: 0.375
This one is D. 3 ounces of seeds and 7 ounces of dried fruit.