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fenix001 [56]
4 years ago
14

Help me ):

Mathematics
1 answer:
slavikrds [6]4 years ago
8 0
For the carpet:

Given:

Area = x^2 + x - 20 ft^2
Length = x+ 5 ft

As a carpet is rectangular, the area is defined as the product of the length and the width. To obtain an expression of the carpet's width, the area is to be divided by the length, which is shown below:

         __x_-_4___x + 5|x^2 + x - 20         x^2 + 5x         --------          -4x - 20          -4x - 20             --------                    0

Therefore, the expression of width = x - 4.

Applying the value of x = 20 to obtain the measurements of the carpet, we obtain the following:

Width = x - 4 = 20 - 4 = 16ft
Length = x+ 5 = 20 + 5 = 25ft.

Therefore, the carpet is 25ft x 16ft.

For the wall:

The same principles apply to the wall as it is also assumed to be rectangular.

Given:
Area = <span>x^2 + 17x + 30 ft^2
Width = x + 2

To obtain the expression for the wall's length, Area is to be divided by the Width, which is shown below:

         __x_+_15______x + 2|x^2 + 17x + 30         x^2 + 2x          --------                  15x + 30                  15x + 30                   ---------                             0

Therefore, the expression for the wall's length is x + 15.

Applying the value of x = 20 to obtain the wall's dimensions:

Length = x + 15 = 20 + 15 = 35ft.
Width = x + 2 = 20 + 2 = 22ft.

Therefore the wall has measurements of 35ft x 22ft.</span>
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A=πr²

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8 0
3 years ago
You are collecting a sample of 60 data points from a population that you know to follow an exponential distribution. It is not a
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Answer:

For the exponential distribution:

\mu = \frac{1}{\lambda}

\sigma^2 = \frac{1}{\lambda^2}

We know that the exponential distribution is skewed but the sample mean for this case using a sample size of 60 would be approximately normal, so then we can conclude that if we have a sample size like this one and an exponential distribution we can approximate the sample mean to the noemal distribution and indeed use the Central Limit theorem.

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

\mu_{\bar X} = \bar X

\sigma_{\bar X}= \frac{\sigma}{\sqrt{n}}

Step-by-step explanation:

The central limit theorem states that "if we have a population with mean μ and standard deviation σ and take sufficiently large random samples from the population with replacement, then the distribution of the sample means will be approximately normally distributed. This will hold true regardless of whether the source population is normal or skewed, provided the sample size is sufficiently large".

For this case we have a large sample size n =60 >30

The exponential distribution is the probability distribution that describes the time between events in a Poisson process.

For the exponential distribution:

\mu = \frac{1}{\lambda}

\sigma^2 = \frac{1}{\lambda^2}

We know that the exponential distribution is skewed but the sample mean for this case using a sample size of 60 would be approximately normal, so then we can conclude that if we have a sample size like this one and an exponential distribution we can approximate the sample mean to the noemal distribution and indeed use the Central Limit theorem.

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

\mu_{\bar X} = \bar X

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4 years ago
Anyone good with geometry?
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5) m<1 = 60
6) m<2 = 30
7) m<3 = m<1 = 60
8) m<4 = m<2 = 30

90/18 = 5
9) m<1 = m<3 = 85
10) m<2 = m<4 = 5

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