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3 years ago

Refer to the qestion below EXPLAIN (f * g * h) (x)

Mathematics

1 answer:

erastovalidia [21]3 years ago

7 0

It's a notation for a function in a function in a function... (f ° g)(x) means f(g(x))
So you use h(x) as x in g(x) then g(x) as x in f(x).

Should get C for an answer.

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A number cube was rolled as part of an experiment. The results are shown in the table. Explain how to find the experimental prob

pychu [463]

The experimental probability of rolling a 6 is 9/60 which can be determined by dividing the frequency of the observation 6 with the total frequency of the experiment.

<u>Step-by-step explanation:</u>

Experimental probability is different from theoretical probability because the former is obtained by experimentation while the latter is what we expect theoretically.When we take a number of observations, the experimental probability and theoretical probability need not be the same.

In this question we have to determine the experimental probability of 6. It can be determined by dividing the frequency of the observation 6 by the total frequency of the experiment.

frequency of 6=9

total frequency=frequency of 1+frequency of 2+frequency of 3+frequency of 4+frequency of 5+frequency of 6

=13+11+9+8+10+9

=60

P(6)=frequency of 6/total frequency

=9/60

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3 years ago

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Answer:A polynomial with integer coefficients that cannot be factored into polynomials of lower degree , also with integer coefficients, is called an irreducible or prime polynomial . Example 1: x2+x+1.

Step-by-step explanation:

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one of the functions equals 0 while the other equals 4

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In a given year, the average annual salary of a NFL football player was $189,000 with a standard deviation of $20,500. If a samp

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Answer:

15.15% probability that the sample mean will be $192,000 or more.

Step-by-step explanation:

To solve this problem, we need to understand the normal probability distribution and the central limit theorem.

Normal probability distribution

Problems of normally distributed samples are 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.

Central Limit Theorem

The Central Limit Theorem estabilishes that, for a random variable X, with mean $\mu$ and standard deviation $\sigma$ , a large sample size can be approximated to a normal distribution with mean $\mu$ and standard deviation $s = \frac{\sigma}{\sqrt{n}}$