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Cerrena [4.2K]
3 years ago
8

In the coordinate plane, what is the length of the line segment that connects points at (0,-1) and (-7, -2)?

Mathematics
1 answer:
notka56 [123]3 years ago
7 0

In the coordinate plane, the length of the line segment that connects points at (0, -1) and (-7, -2) is 7.07 units.

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This is how you do it.

y = a(x - 3)(x - 8) 
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<span>-2 = 36a </span>
<span>- 1 / 18 = a </span>
<span>y = ( - 1 / 18)(x^2 - 11x + 24) </span>
<span>y = (- 1 / 18)x^2 + (11 / 18)x - (3 / 2)
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If this doesn't explain it enough, please, ask questions.
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Can someone help ASAP !!
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Answer:

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Step-by-step explanation:

There is no question, so therefore, I can not help.

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How do you name a prism or pyramid? *
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If we’re talking about the same thing. Determine the shape whether if it’s a prism or pyramid. Then determine what the shape and name of the base.

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A sample size 25 is picked up at random from a population which is normally
Margarita [4]

Answer:

a) P(X < 99) = 0.2033.

b) P(98 < X < 100) = 0.4525

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 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 100 and variance of 36.

This means that \mu = 100, \sigma = \sqrt{36} = 6

Sample of 25:

This means that n = 25, s = \frac{6}{\sqrt{25}} = 1.2

(a) P(X<99)

This is the pvalue of Z when X = 99. So

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

By the Central Limit Theorem

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

Z = \frac{99 - 100}{1.2}

Z = -0.83

Z = -0.83 has a pvalue of 0.2033. So

P(X < 99) = 0.2033.

b) P(98 < X < 100)

This is the pvalue of Z when X = 100 subtracted by the pvalue of Z when X = 98. So

X = 100

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

Z = \frac{100 - 100}{1.2}

Z = 0

Z = 0 has a pvalue of 0.5

X = 98

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

Z = \frac{98 - 100}{1.2}

Z = -1.67

Z = -1.67 has a pvalue of 0.0475

0.5 - 0.0475 = 0.4525

So

P(98 < X < 100) = 0.4525

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How to find slope on a graph?
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