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2 years ago

18 000 mg = ____ kg?It's ez, just me the problem :)

Mathematics

2 answers:

kozerog [31]2 years ago

8 0

0.018kg = 18000mg
1kg = 1000000mg (100k)

hope this helped :)

coldgirl [10]2 years ago

5 0

Answer: 0.018kg

Step-by-step explanation:

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Answer:

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Step-by-step explanation:

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3 years ago

Let a = {1, 2, 3, 4, 5} b = {1, 3, 5} c = {4, 6}. find the cardinality of the given set. 33. n(a) 34. n(b) 35. n(a ⋃

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1) n(a) = 5

The cardinality of a set is the number of elements in that set. So, you simply need to count how many elements belong to the set, and the answer is 5

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Simlarly, in this case you need to count how many elements belong to the set, the answer is 3

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The union of two set is a set composed by all the elements belonging to either one of the sets, with no repetitions. So, since the first set contains all numbers from 1 to 5, the second set contains a 4, which however already belonged to the first set, but also a 6, which is added in the union. So, a ⋃ c = {1,2,3,4,5,6}, and thus n(a ⋃ c)=6.

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The intersection of two sets is a set composed by the elements belonging to both sets. So, 1,2,3 and 5 don't belong to the intersection, because they belong to the first set alone, while 6 doesn't belong to the intersection because they don't belong to the first one. So, a ⋂ c = {4}, and thus n(a ⋃ c)=1.

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3 years ago

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2 years ago

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0.0838 = 8.38% probability of obtaining a sample proportion less than 0.5.

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 z-score 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 p-value, we get the probability that the value of the measure is greater than X.

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