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Lady_Fox [76]
3 years ago
8

Solve the equation by using the Square Root Property. 16x2 + 16x + 4 = 81

Mathematics
1 answer:
Maslowich3 years ago
6 0

Answer:

x = \dfrac{7}{4}, x = \dfrac{-11}{4}              

Step-by-step explanation:

We are given the following equation in the question:

16x^2 + 16x + 4 = 81

We have to find the solution of the equation using square root property.

We can solve the equation as:

16x^2 + 16x + 4 = 81\\(4x)^2 + 2(4x)(2) + (2)^2 = 81\\\text{Identity: }(a+b)^2 = a^2 + b^2 + 2ab\\\Rightarrow (4x+2)^2 = 81\\ \Rightarrow (4x+2) = \sqrt{81}\\ \Rightarrow (4x+2) = \pm 9\\ \Rightarrow (4x+2) = 9, (4x+2) = -9\\\Rightarrow 4x = 7, 4x = -11\\\Rightarrow x = \dfrac{7}{4}, x = \dfrac{-11}{4}

is the required solution of the equation.

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3z+5+5z-5

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a) A large hotel in Miami has 900 rooms (all rooms are equivalent). During Christmas, the hotel is usually fully booked. However
Olegator [25]

Answer:

14.69% probability that this happens

Step-by-step explanation:

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

Normal probability distribution

When the distribution is normal, we use the z-score formula.

In a set with mean \mu and standard deviation \sigma, the zscore of a measure X is given by:

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 pvalue, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem estabilishes 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.

For a proportion p in a sample of size n, the sampling distribution of the sample proportion will be approximately normal with mean \mu = p and standard deviation s = \sqrt{\frac{p(1-p)}{n}}

1000 people were given assurance of a room.

This means that n = 1000

Let us assume that each customer cancels their reservation with a probability of 0.1.

So 0.9 probability that they still keep their booking, which means that p = 0.9

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1 - 0.8531 = 0.1469

14.69% probability that this happens

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