If you have 18 questions how many do you need to get right to have 50 percent correct

1 answer:

5 0

You would need exactly 9 correct questions to get a 50%.

We can find this by multiplying the total number of questions by the percentage we are looking for.

18*50% = 9

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What is the square root of 96<br><br><br><br><br> Follow my new brainly at: fol

Ugo [173]

Answer:

4√6

Step-by-step explanation:

For right now, I will assume that the "<em>Follow my new brainly at..</em>" part is irrelevant.

Anyways, to find the square root of a number, usually the best way is to find its prime factorization first:

96 : $2^5\cdot \:3$

We can rewrite that as:

$\sqrt{2^4\cdot \:2\cdot \:3}$

Simplify!

$2^{\frac{4}{2}}\sqrt{2\cdot \:3}$

Since $2^{\frac{4}{2}}\sqrt{2\cdot \:3}$ is the same thing as $4\sqrt{6}$ , that is our answer.

Thusly, as seen above, the answer is $\boxed{4\sqrt{6}}$ .

Hope this helps! ((;

3 0

3 years ago

Is the confidence interval affected by the fact that the data appear to be from a population that is not normally distributed?

sp2606 [1]

Answer:

D. No, because the sample size is large enough.

Step-by-step explanation:

The central limit theorem states that "if we have a population with mean μ and standard deviation σ and take sufficiently large random samples from the population with replacement, then the distribution of the sample means will be approximately normally distributed. This will hold true regardless of whether the source population is normal or skewed, provided the sample size is sufficiently large".

Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".

If the sample size is higher than 30, on this case the answer would be:

D. No, because the sample size is large enough.

And the reason is given by The Central Limit Theorem since states if the individual distribution is normal then the sampling distribution for the sample mean is also normal.

From the central limit theorem we know that the distribution for the sample mean $\bar X$ is given by: