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3 years ago

What is the greatest common factor for 84 and 56?

1 answer:

6 0

Factors of 56 are: 1, 2, 4, 7, 8, 14, 28, 56

<span>Factors of 84 are: 1, 2, 3, 4, 6, 7, 12, 14, 21, </span>28, 42, 84

The outside term multiplies by the outside term on the oposite side to equal the answer.

Example:
Look at the factors of 84
84X1=84
2X42=84
3X28=84
and so on!

You might be interested in

Solve 5-3^x=-40 round to the nearest ten-thousandth

solong [7]

5-3^x = -40

subtract 5 from each side to get

-3^x=-45

divide both sides by -1 to make them positive

3^x = 45

need to do the natural logarithm on both sides to remove the variable from the exponent

so ln(3^x) = ln(45)

use logarithm rule to move x out

x ln (3) = ln(45)

dive each term by ln(3) then simplify to get

ln(3)/ln(45)

so x = ln(3)/ln(45) which calculates out to 3.46497352

round off to ten thousandths is 3.4650

4 0

3 years ago

Read 2 more answers

Solve for the value of x in this equation: 3x-4=2(3x-5).

zavuch27 [327]

X is equal to 2. The steps are listed below :)

3 0

3 years ago

oksano4ka [1.4K]

I only know A, I’m sorry but I hope it helps.

A) because the highlighted arc is bigger than a semicircle you already know that it’s a major arc, because a minor arc will always be smaller than a semicircle.
A) Major arc

5 0

3 years ago

In the following proportion, what does y equal?

netineya [11]

Y/4 = 39/30

Cross multiply:

30y = (4 x 39)

30y = 156

Divide both sides by 30:

Y = 156/30

Y = 5.2

The answer is C. 5.2

5 0

3 years ago

Read 2 more answers

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