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Alekssandra [29.7K]
3 years ago
8

an aquarium weighs 22.5 lb when empty the aquarium is 30 in long 14 in wide and is filled with water to a depth of 18 in. Water

weighs 0.036 pounds per cubic inch. How much does the aquarium weigh when it's full of water
Mathematics
1 answer:
horrorfan [7]3 years ago
5 0
To get the weight of the aquarium we first calculate the volume of the aquarium.
Volume=(length*width*height)
V=30×14×18
V=7560 in³

But
weight of water is such that for every cubic inches, the weight is 0.036 lb
thus the weight of water will be:
0.036×7560=272.16 lb
Thus the total weight of the aquarium is:
272.16+22.5
=294.66 lb
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The probability solved by binomial distribution that all will survive is 0.8154.

<h3>What is Binomial distribution? </h3>

When each trial has the same probability of achieving a given value, the number of trials or observations is summarized using the binomial distribution. The likelihood of observing a specific number of successful outcomes in a specific number of trials is determined by the binomial distribution.

Computation of probability of all survived people from heart attack;

A heart attack patient has a 0.04 percent chance of dying from the attack (i.e., 4 of 100 die of the attack).

What is the likelihood that each of the five patients who experience a heart attack will survive?

We'll refer to a victory in this scenario as a heart attack (p = 0.04). In other words, we are interested in the likelihood that none of our n=5 patients will die (0 successes).

Each attack has a chance of being fatal or not, with a probability of 4 percent for all patients, and each patient's result is independent.

Assume for the purposes of this example that the five individuals being examined are unrelated, of the same age, and free of any concomitant disorders.

By binomial distribution,

\begin{gathered}P(0 \text { successes })=\frac{5 !}{0 !(5-0) !} 0.04^{0}(1-0.04)^{5-0} \\P(0 \text { successes })=\frac{5 !}{5 !}(1)(0.96)^{5}=(1)(1)(0.8154)=0.8154\end{gathered}

Therefore, with a 4 % chance that anyone will die, there is an 81.54% chance that every patient will survive the onslaught. The outcomes in this example could be 0, 1, 2, 3, 4 or 5 successes (fatalities).

To know more about Binomial Distribution, here

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sleet_krkn [62]
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