How do you find the missing angle of a triangle when all other angles are there?

1 answer:

7 0

All angles in a triangle add up to 180 degrees. So, if you are missing an angle, add up the degrees of the angles present and subtract that from 180. That will be the value of the missing angle in degrees.

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Ariel completes the square for the equation x2 - 16x + 17 = 0. Which of the following equations reveals the vertex of the parabo

stealth61 [152]

X² - 16x + 17 = 0
x² - 16x + 8² - 8² + 17 = 0
(x - 8)² - 64 + 17 = 0
(x - 8)² - 47 = 0

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Answer: y = (x - 8)² - 47 (Answer D)
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8 0

3 years ago

Read 2 more answers

In the right triangle shown, mZA = 45° and AB = 12.<br> B<br> 12<br> 45<br> How long is BC?

wel

Answer:

6sqrt(2)=8.5

Step-by-step explanation:

Using pythagoras theorem

5 0

3 years ago

Subtract 5x - 2 from -3x +4

Solnce55 [7]

Answer:

8x+2

Step-by-step explanation:

Or -8x+2 but i think its 8x+2

4 0

3 years ago

A courier service company wishes to estimate the proportion of people in various states that will use its services. Suppose the

Orlov [11]

Answer:

95.44% probability that the sample proportion will differ from the population proportion by less than 0.03.

Step-by-step explanation:

To solve this question, we need to understand 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.

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 $s = \sqrt{\frac{p(1-p)}{n}}$