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3 years ago

33 is 90% of what number

2 answers:

3 0

$90\%=0,9\\\\\\33:0,9=33:\frac{9}{10}=33\cdot\frac{10}{9}=\frac{110}{3}=36\frac{2}{3}\\\\\\0,9\cdot36\frac{2}{3}=\frac{9}{10}\cdot\frac{110}{3}=33$

riadik2000 [5.3K]3 years ago

3 0

Answer:

36.67

Step-by-step explanation:

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What is the sum of m A and m B if angles A and B are complementary? A. 0° B. 45° C. 90° D. 180°

dlinn [17]

Answer:

c.90

Step-by-step explanation:

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4 years ago

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postnew [5]

Answer:

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Step-by-step explanation:

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3 years ago

If triangle GHI is equilateral and JHK is isosceles, what is the measure of JHK

liubo4ka [24]

Answer:

The measure of JHK is 25 degrees

Step-by-step explanation:

If GHI is equilateral, it means each of the angles are equal to 60 degrees

Thus;

7x - 31 = 60

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7x = 91

x = 91/7

x = 13 degrees

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simply insert the value of x here

Angle JHK = 13 + 12

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3 years ago

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patriot [66]

I think the answer is 9 with 2 remaining

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3 years ago

Executives from Six Flags, a well-known amusement park chain, had interest in constructing a Six Flags theme park in a location

natima [27]

Answer:

The mean of the sampling distribution for the sample proportion when taking samples of size 500 from this population is equal to 0.248.

Step-by-step explanation:

Central Limit Theorem

The Central Limit Theorem establishes 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 $s = \sqrt{\frac{p(1-p)}{n}}$

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This means that $p = \frac{124}{500} = 0.248$

The mean of the sampling distribution for the sample proportion when taking samples of size 500 from this population is equal to

By the Central Limit Theorem, it is equal to the sample proportion of 0.248.

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3 years ago