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2 years ago

Please help!!!! needs to be submitted

Mathematics

1 answer:

Phoenix [80]2 years ago

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The coordinates of the image of triangle are transformation are X = (1,3), Y = (5,-3) and Z = (-1, -7)

<h3>How to determine the transformation?</h3>

From the figure, the coordinates of the triangle are:

A = (-1, -1)

B = (1, 2)

C = (-2,4)

The transformation rule is given as:

$\triangle XYZ = T_{ < 3,1 > }(R_{x-axis}(D_2(\triangle ABC)))$

This means that, we start by dilating the triangle by a scale factor of 2.

So, we have:

A' = (-2, -2)

B' = (2, 4)

C' = (-4,8)

Next, we rotate the triangle across the x-axis

So, we have:

A'' = (-2, 2)

B'' = (2, -4)

C'' = (-4,-8)

Lastly, we translate the triangle by (x + 3,y + 1) to get the triangle XYZ

So, we have:

X = (1,3)

Y = (5,-3)

Z = (-1, -7)

See attachment for the image of the transformation

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Suppose monthly rental prices for a one-bedroom apartment in a large city has a distribution that is skewed to the right with a

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Answer:

a) Nothing, beause the distribution of the monthly rental prices are not normal.

b) 1.43% probability that the sample mean rent price will be greater than $900

Step-by-step explanation:

To solve this question, we need to understand the normal probability distribution and the central limit theorem.

Normal probability distribution

Problems of normally distributed samples are solved using the z-score formula.

In a set with mean $\mu$ and standard deviation $\sigma$ , the zscore of a measure X is given by:

$Z = \frac{X - \mu}{\sigma}$

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the pvalue, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem estabilishes that, for a random variable X, with mean $\mu$ and standard deviation $\sigma$ , the sample means with size n of at least 30 can be approximated to a normal distribution with mean $\mu$ and standard deviation $s = \frac{\sigma}{\sqrt{n}}$

(a) Suppose a one-bedroom rental listing in this large city is selected at random. What can be said about the probability that the listed rent price will be at least $930?

Nothing, beause the distribution of the monthly rental prices are not normal.

(b) Suppose a random sample 30 one-bedroom rental listing in this large city will be selected, the rent price will be recorded for each listing, and the sample mean rent price will be computed. What can be said about the probability that the sample mean rent price will be greater than $900?

Now we can apply the Central Limit Theorem.

$\mu = 880, \sigma = 50, n = 30, s = \frac{50}{\sqrt{30}} = 9.1287$