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pentagon [3]
3 years ago
11

which letter would be the most meaningful variable for the problem situation the weight of a box increase by 5​

Mathematics
1 answer:
Natalija [7]3 years ago
4 0

Answer:i

Step-by-step explanation:

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Ierofanga [76]

Answer:

A: A number line going from negative 2 to positive 8 in increments of 1.Points are at 4 and 8.

Step-by-step explanation:

Hopefully this helps

4 0
3 years ago
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The snow fell 3/4 of an inch every hour. After 12 hours pass, write an equation to represent how many inches of snow has fallen.
lapo4ka [179]

To find the total snow fall you would multiply the amount of snow per hour by the number of hours it snows:

Total = 3/4 inch per hour x hours: Total = 3/4h

h = 12 hours:

Total = 3/4(12) = 9 inches total.

5 0
4 years ago
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A car is traveling at a rate of 48 miles per hour. If the car continues to travel at this same rate, how many hours will it take
MAVERICK [17]
60 hope I’m right hope it helps
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A farmer is monitoring the water content of the soil during a dry summer. The water content of the soil is decreasing at a rate
PolarNik [594]
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6 0
3 years ago
The number of hurricanes that will hit a certain house in the next ten years is Poisson distributed with mean 4. Each hurricane
bulgar [2K]

Answer:

The variance of total loss is 8000000

Step-by-step explanation:

Let

X \to Number of hurricane

Poisson E(X) = 4

Y \to Loss in each hurricane

Exponential E(Y) = 1000

T \to Total Loss

Required

The variance of the total loss

This is calculated as:

Var(T) = Var(E(T|X)) + E(Var(T|X))

Where:

E(T|X) \to Expected total loss given X hurricanes

And it is calculated as:

E(T|X) = E(Y) *N --- Expected Loss in each hurricane * number of loss

Var(T|X) \to Variance of total loss given X hurricanes

And it is calculated as:

Var(T|X) = Var(Y) * N ---- --- Variance of loss in each hurricane * number of loss

So, we have:

Var(T) = Var(E(T|X)) + E(Var(T|X))

Var(T) = Var(E(Y) * N) + E(Var(Y) * N)

For exponential distribution;

Var(Y) = E(Y)^2

So, we have:

Var(T) = Var(E(Y) * X) + E(E(Y)^2 * X)

Substitute values

Var(T) = Var(1000 * X) + E(1000^2 * X)

Simplify:

Var(T) = Var(1000 * X) + 1000^2E(X)

Using variance formula, we have:

Var(T) = 1000^2Var(X) + 1000^2E(X)

For poission distribution:

Var(X) = E(X)

So, we have:

Var(X) = E(X) = 4

The expression becomes:

Var(T) = 1000^2*4 + 1000^2*4

Var(T) = 1000000*4 + 1000000*4

Var(T) = 4000000 + 4000000

Var(T) = 8000000

3 0
3 years ago
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