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GalinKa [24]
3 years ago
5

Which type of transformation is described by (x, y) -> + (x + 3, y — 2)?

Mathematics
1 answer:
creativ13 [48]3 years ago
4 0

Answer:

(x, y) → (x+3, y-2) means Translation 3 units left and 2 units down.

Step-by-step explanation:

Given the rule of transformation:

(x, y) → (x+3, y-2)

If we move horizontally 3 units to the right, 3 is added to the x-coordinate of any given point, and then 2 units down.

In other words,

(x, y) → (x+3, y-2) means Translation 3 units left and 2 units down.

For example,

Let say if we have the point A(4, 5)

Using the translation rule:

(x, y) → (x+3, y-2)

A(4, 5) → A'(4 + 3, 5 - 2) → A'(7, 3)

Therefore, the new position of A(4, 5) after translating 3 units left and 2 units down would be: A'(7, 3)

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Suppose that the walking step lengths of adult males are normally distributed with a mean of 2.5 feet and a standard deviation o
Mumz [18]

Answer:

0% probability that the mean of the sample taken is less than 2.2 feet.

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 normal distributions can be solved using the z-score formula.

In a set with mean \mu and standard deviation \sigma, the z-score 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 p-value, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem establishes that, for a normally distributed random variable X, with mean \mu and standard deviation \sigma, the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean \mu and standard deviation s = \frac{\sigma}{\sqrt{n}}.

For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.

Mean of 2.5 feet and a standard deviation of 0.2 feet.

This means that \mu = 2.5, \sigma = 0.2

Sample of 41

This means that n = 41, s = \frac{0.2}{\sqrt{41}}

Find the probability that the mean of the sample taken is less than 2.2 feet.

This is the p-value of Z when X = 2.2 So

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

By the Central Limit Theorem

Z = \frac{X - \mu}{s}

Z = \frac{2.2 - 2.5}{\frac{0.2}{\sqrt{41}}}

Z = -9.6

Z = -9.6 has a p-value of 0.

0% probability that the mean of the sample taken is less than 2.2 feet.

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Answer:

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