It’s -37 because -20-17=-37
X^a/b is
![\sqrt[b]{x^a}](https://tex.z-dn.net/?f=%20%5Csqrt%5Bb%5D%7Bx%5Ea%7D%20)
. The way I memorise that is x^1/3 is the cubic root of x. Do you get it? In that case, x is raised to a power of 1 and the cubic root is practically has a power of 3.
In your example,
![\sqrt[ \frac{3}{2} ]{16 x^4}](https://tex.z-dn.net/?f=%20%5Csqrt%5B%20%5Cfrac%7B3%7D%7B2%7D%20%5D%7B16%20x%5E4%7D)
is practically square rooting each term then cubing them individually. Remember when square-rooting any index you halve it. I'll elaborate:

=


= 4
Then cube each,

= 64
and

=

As for the 2nd part: you must use the rules of indices.

So breaking the question up:
3 * 3 = 9

stays as is since the 2nd term does not contain x
now:

This makes your final answer look like this:

I hope that helped and good luck in your test!
Answer:
Sample size of 586 or higher.
Step-by-step explanation:
In a sample with a number n of people surveyed with a probability of a success of
, and a confidence level of
, we have the following confidence interval of proportions.

In which
z is the zscore that has a pvalue of
.
The margin of error is:

95% confidence level
So
, z is the value of Z that has a pvalue of
, so
.
What sample size could be 95% confident that the estimated (sample) proportion is within 4 percentage points of the true population proportion?
Sample size of at least n when 
42.1% of women-owned businesses provided retirement plans contributions, which means that
. So







We need a sample size of 586 or higher.
Answer:
x=10
Step-by-step explanation:
First square root 36 cm^2
Then you square both 8 and 6
Then you square root the sum of the two numbers.
This is called Pythagorean Theorem, which can be shown as a^2 + b^2 = c^2
Answer:
d. None of the above.
Step-by-step explanation:
<em>a. By the law of large numbers, it would again be 46%.
</em>
FALSE. This proportion (46%) is a sample statistic, that can or can not be repeated in another sample.
<em>b. By the law of large numbers, the smaller (second) survey will certainly produce a sample proportion farther from the true population proportion than the larger (first) survey.
</em>
FALSE. Smaller samples will produce wider confidence intervals for the estimation of the population proportion, but larger samples does not necessarily gives us better point estimations of the true proportion. A small sample can be closer to the true proportion than a large sample, although is less probable.
<em>c. The proportion computed from the sample of 5000 people would be more accurate because smaller samples tend to be more homogeneous than larger samples.
</em>
FALSE. There is no evidence to claim that smaller samples are more homogeneous.
<em>d. None of the above.</em> TRUE