Define variable in mathematics

if you had a block of each type of wood with the same dimensions,how would their weights compare?explain?

inysia [295]

For this case we have the following equation:
Density = mass / volume
If the volume is the same, then the object with the greatest weight will be the one with the highest density.
You must know the type of way to know the value of the density.
Answer:
The weight of the blocks will depend on the density if the volume is the same.

8 0

3 years ago

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What multiplies up to 53?

Alona [7]

Answer:

13.25 × 4 = 53

Hope this helps

8 0

3 years ago

Find the measure of the missing angle.<br> 34°

Digiron [165]

The answer is 56.

As you can see, the triangle given is a right angle, meaning it is 90 degrees. To find the missing angle, we can solve 90 - 34 to get 56.

hope this helps !! have a nice day :))

5 0

3 years ago

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Please answer quickly thanks

Anastaziya [24]

8/3 = 2 2/3 and -11/3 = -3 2/3
so the middle line is 0 and each big line is 1.
Put the first dot on 2 2/3 or right before the 3rd big line.
Put the second dot on -3 2/3 or the line right after the 4th big line going back from 0

3 0

3 years ago

If a random sample of size nequals=6 is taken from a population, what is required in order to say that the sampling distributio

goldenfox [79]

Answer:

$\bar X \sim N(\mu , \frac{\sigma}{\sqrt{n}})$

In order to satisfy this distribution we need that each observation on this case comes from a normal distribution, because since the sample size is not large enough we can't apply the central limit theorem.

Step-by-step explanation:

For this case we have that the sample size is n =6

The sample man is defined as :

$\bar X = \frac{\sum_{i=1}^n X_i}{n}$

And we want a normal distribution for the sample mean

$\bar X \sim N(\mu , \frac{\sigma}{\sqrt{n}})$

In order to satisfy this distribution we need that each observation on this case comes from a normal distribution, because since the sample size is not large enough we can't apply the central limit theorem.

So for this case we need to satisfy the following condition:

$X_i \sim N(\mu , \sigma), i=1,2,...,n$