What is the measure of X?

Mathematics

1 answer:

vodka [1.7K]3 years ago

5 0

Answer:

Step-by-step explanation:

let the angleAGF be y

then,

y+22=90

y=90-22

y=68

y=x(being vertically oppsite angle

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What is the total surface area of the figure below? Only enter the number part of your answer. Do not include the units.

Leya [2.2K]

Answer:

193.2

Step-by-step explanation:

To solve the triangle:

5.2 × 6= 31.2

You would normally find half of that since it's a triangle but there are two triangles, so each would equal half of 31.2 but if you add both halves that just equals 31.2.

Square one:

9 × 6= 54

All three squares are equal since they have the same measurements.

54 + 54 + 54= 162

162 + 31.2= 193.2

4 0

2 years ago

a) A large hotel in Miami has 900 rooms (all rooms are equivalent). During Christmas, the hotel is usually fully booked. However

Olegator [25]

Answer:

14.69% probability that this happens

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