Can some one help me with this question its hard :( down below ill give brainliest

Mathematics

1 answer:

german3 years ago

4 0

Answer:

The Answer is A. 8

Explanation:

For Answer Choice A:

There's nothing to explain here. It's just the number 8.

For Answer Choice B:

2 · 2 · 2 · 2 is 16. That symbol ( · ) is another way of writing the multiplication symbol. So this problem is essentially 2 x 2 x 2 x 2, which equals 16.

For Answer Choice C:

Well, this one needs no explanation either. It's just the number 16.

For Answer Choice D:

$2^{4}$ is also equal to 16. I'm too lazy to explain how to do this one so yea.

Hope this helps you. Uh, yea. Have a good day? omg I'm so tired

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Please help with math please.

jonny [76]

Subtract the constant vector that is on the left. The arithmetic applies to corresponding elements of the vectors.

$A=\left[\begin{array}{cccc}0&1&-2&3\end{array}\right] -\left[\begin{array}{cccc}11&17&-8&13\end{array}\right]\\\\A=\left[\begin{array}{cccc}-11&-16&6&-10\end{array}\right]\\\\a_{11}=-11\\a_{12}=-16\\a_{13}=6\\a_{14}=-10$

5 0

3 years ago

The average score of all golfers for a particular course has a mean of 71 and a standard deviation of 3. Suppose 36 golfers play

Zielflug [23.3K]

Answer:

.0228

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 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 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 $s = \frac{\sigma}{\sqrt{n}}$ .