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Alex777 [14]
3 years ago
11

Which estimate best describes the area under the curve in square units?

Mathematics
2 answers:
zheka24 [161]3 years ago
5 0
B) 15 units^2
each box equals 0.5 × 1 = 0.5 units^2, so add up how many boxes, and it's close to around 30, so 30×0.5 = 15 units^2
just olya [345]3 years ago
4 0
The correct answer is 15 units^2 
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6.3 divided by 2.5 HELP ME PLZZZZZZZZZZZZZZZ
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6.3/2.5=2.52

Step-by-step explanation:

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What equation represents the nth term of the sequence {2, -1, -4, -7, ...}
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Suppose that 20% of the residents in a certain state support an increase in the property tax. An opinion poll will randomly samp
aleksklad [387]

Answer:

95.44% probability the resulting sample proportion is within .04 of the true proportion.

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 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.

For the sampling distribution of the sample proportion in sample of size n, the mean is \mu = p and the standard deviation is s = \sqrt{\frac{p(1-p)}{n}}

In this question:

p = 0.2, n = 400

So

\mu = 0.2, s = \sqrt{\frac{0.2*0.8}{400}} = 0.02

How likely is the resulting sample proportion to be within .04 of the true proportion (i.e., between .16 and .24)?

This is the pvalue of Z when X = 0.24 subtracted by the pvalue of Z when X = 0.16.

X = 0.24

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

By the Central Limit Theorem

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

Z = \frac{0.24 - 0.2}{0.02}

Z = 2

Z = 2 has a pvalue of 0.9772.

X = 0.16

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

Z = \frac{0.16 - 0.2}{0.02}

Z = -2

Z = -2 has a pvalue of 0.0228.

0.9772 - 0.0228 = 0.9544

95.44% probability the resulting sample proportion is within .04 of the true proportion.

6 0
3 years ago
Read 2 more answers
How do I solve this??
tresset_1 [31]

Answer:

x = 7.5

Step-by-step explanation:

Since DF bisects ∠CDE then

∠EDF = ∠CDF, hence

8x = 4x + 30 ( subtract 4x from both sides )

4x = 30 ( divide both sides by 4 )

x = 7.5


4 0
3 years ago
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