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3 years ago

Plz help me help me help me

Mathematics

2 answers:

yulyashka [42]3 years ago

8 0

Answer:

Step-by-step explanation: 16 is for the apple juice for when the orange juice is 8 since orange juice is half of the apple juice. For the last one the apple juice is 32 and the orange juice is 16

exis [7]3 years ago

7 0

The answer is this :) hope I helped

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Five times the sum of a number and 6 is 48

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3 years ago

Please answer!!!!!!

Crazy boy [7]

Answer:

(this isnt a mathematics question, it's science, btw)

Indeed, you are correct. It is 1. I think.

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According to a Yale program on climate change communication survey, 71% of Americans think global warming is happening.† (a) For

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Answer:

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Step-by-step explanation:

Binomial probability distribution

The binomial probability is the probability of exactly x successes on n repeated trials, and X can only have two outcomes.

$P(X = x) = C_{n,x}.p^{x}.(1-p)^{n-x}$

In which $C_{n,x}$ is the number of different combinations of x objects from a set of n elements, given by the following formula.

$C_{n,x} = \frac{n!}{x!(n-x)!}$

And p is the probability of X happening.

The expected value of the binomial distribution is:

$E(X) = np$

The standard deviation of the binomial distribution is:

$\sqrt{V(X)} = \sqrt{np(1-p)}$

Normal probability distribution

Problems of normally distributed samples can be solved using 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.

When we are approximating a binomial distribution to a normal one, we have that $\mu = E(X)$ , $\sigma = \sqrt{V(X)}$ .