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svlad2 [7]
3 years ago
5

Elena went to the store where you can scoop your own popcorn and buy as much as you want.she brought 10 ounces of spicy popcorn

for $2.50.How much popcorn can you buy per dollar?

Mathematics
1 answer:
kifflom [539]3 years ago
7 0

Answer:

A. 4 ounces

Step-by-step explanation:

Given that Elana paid $2.50 for 10 ounces of popcorn, let x be the amount popcorn a dollar will buy.

Thus we have:

$2.50 = 10 ounces

$1 = x

Cross multiply

$2.50 × x = 10 ounces × $1

Divide both sides by $2.50

($2.50 × x)/$2.50 = (10 ounces × $1)/$2.50

x = (10 ounces)/2.50

x = 4 ounces

Therefore, you can buy 4 ounces of popcorn per dollar

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zalisa [80]

No, (2,2) is not solution of y < 4x - 6

Step-by-step explanation:

To solve above linear problem, we use standard method of  Back- Substitution.

First of all, A point (2,2) only becomes solution of equation, if it satisfies equation y < 4x - 6.

Here, the  y < 4x - 6  seems to be like a linear equation.

In this Equation, using  Back- Substitution method,

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4 years ago
A researcher reports survey results by stating that the standard error of the mean is 25 the population standard deviation is 40
bezimeni [28]

Answer:

a) A sample of 256 was used in this survey.

b) 45.14% probability that the point estimate was within ±15 of the population mean

Step-by-step explanation:

This question is solved using 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.

a. How large was the sample used in this survey?

We have that s = 25, \sigma = 400. We want to find n, so:

s = \frac{\sigma}{\sqrt{n}}

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