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lana66690 [7]
3 years ago
13

Which graph represents x

Mathematics
2 answers:
Paraphin [41]3 years ago
6 0
Hey!


First, we need to multiply both sides of the equation by 3 to give us x on its own.

<em>Equation :</em>
\frac{3x}{3} \leq 2 * 3

<em>Solution {Equation Solved} :</em>
x \leq 6

So the given number for our new inequality is 6 so that's where we'll plot our point.

<u><em>REMEMBER IT HAS TO BE A CLOSED POINT</em></u>

Now we need to find the direction of the arrow. Since the "teeth" of the symbol are facing right, this means our arrow will be going left.

<em>So, this means the correct </em><span><em>graph </em></span><em>to the given </em><em>is</em>  the second number line.

Hope this helps!


- Lindsey Frazier ♥
Maurinko [17]3 years ago
4 0
The answer is the second graph.

\frac{x}{3} \leq 2
You need to isolate x, in order to do this you need to multiply both sides by 3, or 3/1
2*3 = 6
x/3 * 3 = x
Your equation should now be
x \leq 6 

So the circle should be filled in and pointing towards the left starting from 6
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Answer:

0.9452 = 94.52% probability that their mean length is less than 16.8 inches.

Step-by-step explanation:

To solve this question, we need to understand the normal probability distribution and the central limit theorem.

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Problems of normal distributions can be solved using the z-score formula.

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Z = \frac{X - \mu}{\sigma}

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the p-value, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem establishes that, for a normally distributed random variable X, with mean \mu and standard deviation \sigma, the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean \mu and standard deviation s = \frac{\sigma}{\sqrt{n}}.

For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.

Mean of 15.4 inches, and standard deviation of 3.5 inches.

This means that \mu = 15.4, \sigma = 3.5

16 items are chosen at random

This means that n = 16, s = \frac{3.5}{\sqrt{16}} = 0.875

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Z = 1.6

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