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Firlakuza [10]
3 years ago
15

A random sample of cats at the SPCA shows that 25 out of the 40 cats are kittens. There are a total of 800 cats at all the anima

l shelters in the area. Using this information, predict the number of kittens.
Mathematics
1 answer:
Alinara [238K]3 years ago
7 0

Answer: The number of kittens= 500.

Step-by-step explanation:

Given, A random sample of cats at the SPCA shows that 25 out of the 40 cats are kittens.

i.e. ratio of kitten to cats =\dfrac{25}{40}=\dfrac{5}{8}

We assume that , the ratio will remains same.

Let x be the number of kitten out of 800 , then we have

\dfrac{x}{800}=\dfrac{5}{8}\\\\\Rightarrow\ x=\dfrac{800\times5}{8}=500

Hence, the number of kittens out of 800 cats= 500.

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Answer:

The probability is 0.789

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 normally distributed samples are solved using 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.

In this problem, we have that:

\sigma = 16, n = 400, s = \frac{16}{\sqrt{400}} = 0.8

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Using \mu = 30, this is the pvalue of Z when X = 31 subtracted by the pvalue of Z when X = 29. So

X = 31

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By the Central Limit Theorem

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

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Z = 1.25

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

Z = \frac{29 - 30}{0.8}

Z = -1.25

Z = -1.25 has a pvalue of 0.1056

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