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sattari [20]
3 years ago
5

What is the value of 3 1/3 divided by 3/16

Mathematics
2 answers:
maksim [4K]3 years ago
7 0

Answer:

17.7777777778

Step-by-step explanation:

USPshnik [31]3 years ago
3 0

Answer:0.2152778

Step-by-step explanation:

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Sladkaya [172]
Area = b*h and in some cases * 2 so you need to do this, 64*52 + 40*28= 4448 yd2
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3 years ago
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Simplify m(2+n-m)+3(3n+m^2-1)
mina [271]

Answer:2m^2+mn+2m+9n-3

Step-by-step explanation:

m(2+n-m)+3(3n+m^2-1)

open brackets

2m+mn-m^2+9n+3m^2-3

Collect like terms

3m^2-m^2+mn+2m+9n-3

2m^2+mn+2m+9n-3

4 0
3 years ago
How do you solve this?
Mila [183]

Answer:

Multiply 60 by .40

60 * .40 = 24

Answer is B


Step-by-step explanation:


5 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}}

In this question:

p = 0.05, n = 212, \mu = 0.05, s = \sqrt{\frac{0.05*0.95}{212}} = 0.015

What is the probability that the sample proportion will differ from the population proportion by less than 0.03?

This is the pvalue of Z when X = 0.03 + 0.05 = 0.08 subtracted by the pvalue of Z when X = 0.05 - 0.03 = 0.02. So

X = 0.08

Z = \frac{X - \mu}{\sigma}

By the Central Limit Theorem

Z = \frac{X - \mu}{s}

Z = \frac{0.08 - 0.05}{0.015}

Z = 2

Z = 2 has a pvalue of 0.9772

X = 0.02

Z = \frac{X - \mu}{s}

Z = \frac{0.02 - 0.05}{0.015}

Z = -2

Z = -2 has a pvalue of 0.0228

0.9772 - 0.0228 = 0.9544

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

6 0
3 years ago
Plz help!!! due tonight I need help!!!!​
Pachacha [2.7K]

Answer:

14400

Step-by-step explanation: i culd be wrong tho

3 0
3 years ago
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