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SOVA2 [1]
3 years ago
15

Tony is opening a pizza restaurant and needs to determine the amounts of ingredients he will use in his pizzas. He wants the amo

unt of dough for a medium pizza to be 3/2 times the amount of dough for a small pizza. And, he wants the amount of dough for a large pizza to be 5/2 times the amount of dough for a small pizza. Also, the amount of dough used for one large pizza should to be equal to the amount of dough used for one medium and one small pizza. So, he needs to determine how much dough to use for a small pizza. Use this information to complete the following tasks.
Question 1:


Part A:

Write an equation to represent this situation with the variable s representing the amount of dough needed for a small pizza.


Part B:

Combine like terms without moving terms across the equal sign in the equation from part A.


Part C:

Now, use opposite operations on the equation from part B to move one of the terms so they are both on the same side of the equal sign.


Part D:

Combine the like terms in the equation from part C.
Mathematics
1 answer:
kupik [55]3 years ago
3 0

Answer:

hi so what i would do to find out the small pizza dough is

Step-by-step explanation:

1. find out what 5/2 is=2.5

2.find out what 3/2 is=1.5

3.subtract 2.5-1.5=s

4.s=1

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Answer:

0.0838 = 8.38% probability of obtaining a sample proportion less than 0.5.

Step-by-step explanation:

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

Normal Probability Distribution

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

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

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

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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.

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}}

Proportion of 0.6

This means that p = 0.6

Sample of 46

This means that n = 46

Mean and standard deviation:

\mu = p = 0.6

s = \sqrt{\frac{p(1-p)}{n}} = \sqrt{\frac{0.6*0.4}{46}} = 0.0722

Probability of obtaining a sample proportion less than 0.5.

p-value of Z when X = 0.5. So

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

By the Central Limit Theorem

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

Z = \frac{0.5 - 0.6}{0.0722}

Z = -1.38

Z = -1.38 has a p-value of 0.0838

0.0838 = 8.38% probability of obtaining a sample proportion less than 0.5.

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