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AlexFokin [52]
3 years ago
13

Henry made a scale model of a submarine. The submarine has an actual height of 75 feet. Henry’s model used a scale in which 1 in

ch represents 5 feet. What is the height in inches of Henry's model?
Mathematics
1 answer:
dimulka [17.4K]3 years ago
3 0

Answer: 180 Inchs

Reason:

75 / 5

180 Inchs = 15 Feet

-DoggyMan5

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Ket [755]

Answer:

  B.  Step 2 uses the associative property, and step 3 uses the commutative property.

Step-by-step explanation:

The associative property lets you group terms for addition any way you like. It appears that in Step 2, the grouping is ...

  4 + (1/6 + 3) + 5/6

The commutative property lets you change the order of any pair of terms involved in addition. It appears that in Step 3, the order of the terms within the group has been swapped.

  4 + (3 + 1/6) + 5/6

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<em>Comment on associative and commutative properties</em>

What applies to terms in addition applies to factors in multiplication.

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Converted to a decimal it’s -0.8
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Step-by-step explanation:

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2 years ago
A researcher reports survey results by stating that the standard error of the mean is 25 the population standard deviation is 40
bezimeni [28]

Answer:

a) A sample of 256 was used in this survey.

b) 45.14% probability that the point estimate was within ±15 of the population mean

Step-by-step explanation:

This question is solved using the normal probability distribution and the central limit theorem.

Normal probability distribution

When the distribution is normal, we use 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 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.

a. How large was the sample used in this survey?

We have that s = 25, \sigma = 400. We want to find n, so:

s = \frac{\sigma}{\sqrt{n}}

25 = \frac{400}{\sqrt{n}}

25\sqrt{n} = 400

\sqrt{n} = \frac{400}{25}

\sqrt{n} = 16

(\sqrt{n})^2 = 16^2[tex][tex]n = 256

A sample of 256 was used in this survey.

b. What is the probability that the point estimate was within ±15 of the population mean?

15 is the bounds with want, 25 is the standard error. So

Z = 15/25 = 0.6 has a pvalue of 0.7257

Z = -15/25 = -0.6 has a pvalue of 0.2743

0.7257 - 0.2743 = 0.4514

45.14% probability that the point estimate was within ±15 of the population mean

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I think it’s B but don’t at me
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