The correct graph is graph 4.
You can find this by looking for recognizable points. This is the easiest way to find composite graphs. In the graph f(x) =|x − 5| + |x − 4| both 5 and 4 yield the answer of 1. You can see that in the examples below:
<span>f(x) =|x − 5| + |x − 4|
</span><span>f(5) =|5 − 5| + |5 − 4|
</span><span>f(5) =|0| + |1|
f(5) = 0 + 1
f(5) = 1
</span><span>f(x) =|x − 5| + |x − 4|
</span><span>f(4) =|4 − 5| + |4 − 4|
</span><span>f(4) =|-1| + |0|
f(4) = 1 + 0
f(4) = 1
Now we can look for graphs that have those two reference points. The 4th graph is the only one that has those. </span>
Answer:
<u>x = -1</u>
Step-by-step explanation:
3(x - 6) - 8x = -2 + 5(2x + 1)
3x - 18 - 8x = -2 + 10x + 5
-5x - 18 = 10x + 3
-15x = 15
<u>x = -1</u>
Answer:
m∠KHL = 43°
Step-by-step explanation:
From the picture attached,
m∠KHL ≅ m∠GHL [Given in the picture]
Now substituting the values of the angles,
(3x + 1) = (5x - 27)
1 + 27 = 5x - 3x
28 = 2x
14 = x
m∠KHL = (3x + 1)° = (3 × 14) + 1
= 42 + 1
= 43°
Therefore, measure of ∠KHL = 43°
To resolve the proposed issue, an explanation is needed in which the subject is addressed
Answer:
B
Step-by-step explanation:
Consider an event A happening. If we do not have enough data to estimate its actual probability, we may choose a range 0.6 to 0.9 as a first case which indicates we are quite sure it will most likely occur. If however, we have enough data, we may estimate a range of 0.7 to 0.8 as a second case that is more certain on its actual likelihood of occurrence.
Say the actual probability of the event is given as 0.75, in the first case, we can infer the probability interval as 0.75 ± 0.15 (as 0.75-0.15=0.6 and 0.75+0.15=0.9 for the lower and upper bounds respectively). In the second case, we can infer the probability interval as 0.75±0.05 (as 0.75-0.05=0.7 and 0.75+0.05=0.8 for the lower and upper bounds respectively).
Thus, we can see that with more certainty of the event happening (with more data in this case), the probability or prediction intervals are lower.
Hence, in the experiment, we will observe a narrower prediction interval for researcher A who has more (twice as many points) data than researcher B who has fewer points.
