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stellarik [79]
3 years ago
12

0.5 kilograms (kg) is equal to how many ounces? Round your answer to the nearest tenth.

Mathematics
2 answers:
enot [183]3 years ago
3 0

Answer:

17.6 ounces per 500 g

Step-by-step explanation:

Knowing one ounce equals 28.34 g, all we have to do is divide half a kilo (500 g) by the equivalent of an ounce to get the number of ounces in a kg.  So,

O = 500 g / 28.34g/ounce = 17.64 ounces

We round that to the nearest tenth... to get 17.6 ounces per 500 g

Converting between metric and imperial/US system is always complicated.

Gwar [14]3 years ago
3 0

Answer:

0.5 kilograms (kg) is equal to 17.6 ounces

Step-by-step explanation:

We have

                    1 kg = 35.274 ounces

                   0.5 kg = 0.5 x 35.274 = 17.637 ounces

Rounding to nearest tenth

              0.5 kg = 17.637 ounces   = 17.64 ounces = 17.6 ounces

So we have 0.5 kilograms (kg) is equal to 17.6 ounces    

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Answer:

Probability that the sample proportion will be greater than 0.5 is 0.8133.

Step-by-step explanation:

We are given that the a particular candidate for public office is in fact favored by p = 48% of all registered voters. A polling organization is about to take a simple random sample of voters and will use the sample proportion to estimate p.

Suppose that the polling organization takes a simple random sample of 500 voters.

<em>Let </em>\hat p<em> = sample proportion</em>

The z-score probability distribution for sample proportion is given by;

               Z = \frac{ \hat p-p}{\sqrt{\frac{\hat p(1-\hat p)}{n} } }  ~ N(0,1)

where, \hat p = sample proportion

           p = population proportion = 48%

           n = sample of voters = 500

The Z-score measures how many standard deviations the measure is away from the mean. After finding the Z-score, we look at the z-score table and find the p-value (area) 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.

So, probability that the sample proportion will be greater than 0.5 is given by = P( \hat p > 0.50)

  P( \hat p > 0.50) = P( \frac{ \hat p-p}{\sqrt{\frac{\hat p(1-\hat p)}{n} } } < \frac{0.50-0.48}{\sqrt{\frac{0.50(1-0.50)}{500} } } ) = P(Z < 0.89) = 0.8133

<em>Now, in the z table the P(Z </em>\leq<em> x) or P(Z < x) is given. So, the above probability is calculated by looking at the value of x = 0.89 in the z table which has an area of 0.8133.</em>

Therefore, probability that the sample proportion will be greater than 0.50 is 0.8133.

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