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Tju [1.3M]
2 years ago
9

X + (-x) = 0 help me plz​

Mathematics
1 answer:
andriy [413]2 years ago
7 0

x+-x = 0

x-x = 0

0 = 0

infintely many solutions

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Pleeeeease help me out!!!!!
Nataly_w [17]

If you look at the data, you can see that every y value is opposite to the corresponding x- value.

So, equation can be written as

y = [-] x or y=[-1]x

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3 years ago
Find the missing angle.
Gekata [30.6K]

Answer:

total angle of a triangle = 180°

other two angles = 85° , 65°

∴ the missing angle = 180 - ( 85 + 65 )

                                 = 180 - 150

                                 = 30

hope this answer helps you..

please mark as brainliest... thank you!

6 0
3 years ago
Read 2 more answers
A small kite starts at 7.3 meters off the ground and goes up at 2.5 meters per second. A large kite starts at 12.4 meters off th
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Answer:

at 25 seconds

Step-by-step explanation:

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3 years ago
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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
Hitman42 [59]

Answer:

0.9544 = 95.44% probability of the resulting sample proportion being 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 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.

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

20% of the residents in a certain state support an increase in the property tax. Sample of 400.

This means that p = 0.2, n = 400

Mean and standard deviation:

Mean \mu = p = 0.2

Standard deviation s = \sqrt{\frac{p(1-p)}{n}} = \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. So

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

0.9544 = 95.44% probability of the resulting sample proportion being within .04 of the true proportion

5 0
3 years ago
Help me answer this..<br><br>​
kipiarov [429]

Step-by-step explanation:

you are you reading me in the morning and will be there by tomorrow morning and will be there by tomorrow morning and will

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