Answer:
Kara should plot points where the arcs intersect above and below the line segment.
Step-by-step explanation:
The bisection of a line segment is the dividing of the line segment into two equal parts. To bisect a line segment, you have place your compass on the endpoints and measure a distance greater than half of the segment. The point of intersection of the arcs both above and below the segment is then joined thereby bisecting the line.
Since Kara has already drawn the two arcs which are greater that half of the length, all Kara needs to do is plot points where the arcs intersect above and below the line segment.
Remember, a number's additive inverse is simply its opposite.
Let's say we have a number a.
The opposite of a is -a, and the opposite of -a is a.
Thus, the additive inverse of
is
Hope it helps.
~Just a felicitous girl
#HaveAnAmazingDay
Feel free to ask if you have any doubts.
If the base is 2 and the exponent is the the expression would look like this → 2³. In order to simplify it, you have to multiply the base number by the number of the exponent.
2×2×2=8
Therefore, the simplified expression would be 8.
I hope this helped.
Answer:
1) 4
2) 1.5
3) 8
4) 1
5) 5
6) 9
Step-by-step explanation:
<h3>
1)</h3><h3>
√16</h3>
= √(4x4)
= √(4)²
<h3>= 4</h3>
<h3>
2)</h3><h3>
√2.25</h3>
= √(9/4)
= √(3x3)/(2x2)
= √(3)²/(2)²
= 3/2
<h3>= 1.5</h3>
<h3>3)</h3><h3>6² ÷ 9 x 2 </h3>
= 36 ÷ 9 x 2
= 36 x 1/9 x 2
= 36/9 x 2
= 12/3 x 2
= 4 x 2
<h3>= 8</h3>
<h3>4)</h3><h3>12-2 / 6+4</h3>
= 10/10
<h3>= 1</h3><h3 /><h3>5)</h3><h3>√(16+9)</h3>
= √(25)
= √(5x5)
= √(5)²
<h3>= 5</h3><h3 /><h3>6)</h3><h3>63 ÷ 3² + |2|</h3>
= 63 ÷ 9 + |2|
= 63/9 + |2|
= 21/3 + |2|
= 7 + |2|
<h3>= 9</h3>
Answer:
The sample size is 
Step-by-step explanation:
From the question we are told that
The margin of error is E = 1.5 seconds
The standard deviation is s = 4 seconds
Given that the confidence level is 97% then the level of significance is mathematically represented as
=> 
Generally from the normal distribution table the critical value of 
is
Generally the sample size is mathematically represented as
![n =[ \frac{Z_{\frac{\sigma }{2 } } * \sigma }{E} ]^2](https://tex.z-dn.net/?f=n%20%20%3D%5B%20%20%5Cfrac%7BZ_%7B%5Cfrac%7B%5Csigma%20%7D%7B2%20%7D%20%7D%20%2A%20%20%5Csigma%20%7D%7BE%7D%20%5D%5E2)
=> ![n =[ \frac{2.17 * 4 }{1.5} ]^2](https://tex.z-dn.net/?f=n%20%20%3D%5B%20%20%5Cfrac%7B2.17%20%20%2A%204%20%7D%7B1.5%7D%20%5D%5E2)
=>
