Probability is the chance that some event will occur or a guarantee.

1 answer:

6 0

Probability means possibility. It is a branch of mathematics that deals with the occurrence of a random event. The value is expressed from zero to one

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Nov 19 F

Digiron [165]

Answer:

l.mao you could just send a picture, it's a bit hard to actually answer any of the questions if it's jam-packed like that

6 0

3 years ago

Help, please simplify.. thank you

Romashka [77]

$\bf \cfrac{3.7\times 10^{-12}(42)}{(.000000025)(4.0\times 10^{-15})}\implies 
\cfrac{37\times 10^{-13}(42)}{(25\times 10^9)(4\times 10^{-15})}$

$\bf \cfrac{37(42)\times 10^{-13}}{25(4)\times 10^910^{-15}}\implies 
\cfrac{1554\times 10^{-13}}{100\times 10^{9-15}}\implies 
\cfrac{1554\times 10^{-13}}{1\times 10^2\times 10^{9-15}}
\\\\\\
\cfrac{1554\times 10^{-13}}{10^{2+9-15}}\implies \cfrac{1554\times 10^{-13}}{10^{-4}}\implies 1554\times 10^{-13}\times 10^4
\\\\\\
1554\times 10^{-13+4}\implies 1554\times 10^{-9}\implies \cfrac{1554}{10^9}\implies 0.000001554$

8 0

4 years ago

In the United States, 7% of all registered voters belong to the Green party. A random sample of 50 registered voters is taken. U

Maksim231197 [3]

Answer:

The expected value of the sample proportion is of 0.07.

1. P(p < .02) = 0.0823

2. P(p > .15) = 0.0132

3. P(.05 < p < .09) = 0.4176

Step-by-step explanation:

This question is solved using 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

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