50 = x^2
To isolate x you need to do the opposite of an exponent, which would be square root.
![\sqrt{50} = x Next solve what the square root of 50 is[tex]5\sqrt{2}](https://tex.z-dn.net/?f=%5Csqrt%7B50%7D%20%3D%20x%20%3C%2Fp%3E%3Cp%3ENext%20solve%20what%20the%20square%20root%20of%2050%20is%3C%2Fp%3E%3Cp%3E%5Btex%5D5%5Csqrt%7B2%7D)
= x
That is the exact form, in other words your answer, if teacher is looking for a decimal, it would be
7.07 = x
Answer:
1,100 is the answer.
Step-by-step explanation:
Since the difference of the subtraction problem is 1,103, when it is rounded, it becomes 1,100.
Answer:
It's 218, since 4 rounds below 5.
Step-by-step explanation: Please brainliest!
A) angle 1 = angle 2 - Incorrect
These angles are only on one of the following lines, which means that they do not show that lines C and D are parallel. In addition, angles 1 and 2 are supplementary, so they could not be equal.
B) angle 1 + angle 2 = 180 - Incorrect
Though this is true, this does not prove that lines C and D are parallel because both of these angles are on the same line.
C) angle 2 = angle 3 - Correct
These angles are alternate interior angles and there are angles on both lines, which proves that lines C and D are parallel.
D) angle 2 + angle 3 = 180 - Incorrect
These angles are equivalent, so they cannot add up to 180 degrees unless the intersecting line was perpendicular to both lines C and D.
Hope this helps!! :)
Assuming that the null hypothesis is true, then<u> p-Value</u> is the probability of observing a value of the test statistic that is at least as extreme as the value actually computed from the sample data.
Step-by-step explanation:
<u>The p-value is the probability that a random sample that is selected produces as the value of the test statistic is at least as extreme as the observed value when H0 is true.</u>
<u>The p-value as measure of how surprising our result is if the null hypothesis is true.</u>
So ,we can state that- Assuming that the null hypothesis is true, then<u> p-Value</u> is the probability of observing a value of the test statistic that is at least as extreme as the value actually computed from the sample data.
