Whoever can answer this will get brainlyest

Mathematics

1 answer:

azamat3 years ago

6 0

Answer:

The proportion is $\frac{35}{108}$ = $\frac{x}{84}$ , and the length of the unknown side is $\frac{245}{9}$ or 27.22 approximately rounded to the nearest hundredth.

Step-by-step explanation:

Since the triangles are similar, the proportion of corresponding sides are equal. The pairs of corresponding sides in these triangles which we'll use to solve it are LK, KE, MK, and KF. LK and KE are corresponding sides, and their proportion is $\frac{35}{108}$ . MK and KF are corresponding sides, and their proportion is $\frac{x}{84}$ in which x represents the missing side. The proportions are equal, so $\frac{35}{108}$ = $\frac{x}{84}$ . Multiply both sides by 84 to isolate the variable, and you'll get $x = \frac{35*84}{108}$ , which is $\frac{2940}{108}$ or $\frac{245}{9}$ .

You might be interested in

Some help please! :) Branliest

sveta [45]

Answer:

Households without a vehicle were not surveyed. D

Step-by-step explanation:

I just finished with Maps testing like 10 minutes ago XD good luck!!

6 0

3 years ago

Consider the question of whether the home team wins more than half of its games in the National Basketball Association. Suppose

spayn [35]

Answer:

0.0037 = 0.37% probability that the home team would win 65% or more of its games in a simple random sample of 80 games

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