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astra-53 [7]
2 years ago
7

(02.02)A shape is shown on the graph:

Mathematics
1 answer:
inna [77]2 years ago
8 0

Based on the shape shown on the graph, the reflection of the shape would be b) A coordinate grid is shown from positive 8 to negative 8 on the x-axis and from positive 8 to negative 8 on the y-axis. A triangle is shown on ordered pair 2, negative 2 and 4, negative 2, and 2, negative 6.

<h3>What shape is the reflection?</h3>

The coordinate grid that the shape would be reflected across is the y axis to get a reflection in the negative side of the x axis.

The first reflection would be of the point that is (2,2). It would be reflected to become (-2, 2).

The second reflection would be of point (2, 6) which would then be reflected to become (-2, 6).

Finally the third point would be of point (4, 2) which would then become (-4, 2).

In conclusion, the reflection was across the y axis.

Options include:

  • a) A coordinate grid is shown from positive 8 to negative 8 on the axis and from positive 8 to negative 8 on the y-axis. A triangle is shown on ordered pair negative 2, 2 and negative 4, 2 and negative 4, 6.
  • b) A coordinate grid is shown from positive 8 to negative 8 on the x-axis and from positive 8 to negative 8 on the y-axis. A triangle is shown on ordered pair 2, negative 2 and 4, negative 2, and 2, negative 6.
  • c) A coordinate grid is shown from positive 8 to negative 8 on the axis and from positive 8 to negative 8 on the y-axis. A triangle is shown on ordered pair negative 2, 2 and negative 2, 4 and negative 6, 2.
  • d) A coordinate grid is shown from positive 8 to negative 8 on the axis and from positive 8 to negative 8 on the y-axis. A triangle is shown on ordered pair 2, negative 2 and 2, negative 4 and 6, negative 2.

Find out more on reflection at brainly.com/question/1908648

#SPJ1

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Answer:

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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:

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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.

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The Central Limit Theorem estabilishes 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 36 hours and a standard deviation of 5.5 hours.

This means that \mu = 36, \sigma = 5.5

a. What can you say about the shape of the distribution of the sample mean?

By the Central Limit Theorem, it is approximately normal.

b. What is the standard error of the distribution of the sample mean? (Round your answer to 4 decimal places.)

Sample of 9 means that n = 9. So

s = \frac{\sigma}{\sqrt{n}} = \frac{5.5}{\sqrt{9}} = 1.8333

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c. What proportion of the samples will have a mean useful life of more than 38 hours?

This is 1 subtracted by the pvalue of Z when X = 38. So

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

By the Central Limit Theorem

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

Z = \frac{38 - 36}{1.8333}

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1 - 0.8621 = 0.1379

0.1379 = 13.79% of the samples will have a mean useful life of more than 38 hours.

d. What proportion of the sample will have a mean useful life greater than 34.5 hours?

This is 1 subtracted by the pvalue of Z when X = 34.5. So

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

Z = \frac{34.5 - 36}{1.8333}

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1 - 0.2061 = 0.7939

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pvalue of Z when X = 38 subtracted by the pvalue of Z when X = 34.5. So

0.8621 - 0.2061 = 0.656

0.656 = 65.6% of the samples will have a mean useful life between 34.5 and 38 hours

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