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slava [35]
1 year ago
9

How many times larger is the 8 in 184.36 than 9,027.83

Mathematics
1 answer:
Sunny_sXe [5.5K]1 year ago
4 0

Answer: Thus, the number with the larger value of 8 is 184.36 and it is 100 times larger.

Step-by-step explanation:

First we can find out the place value,

Then we can write,

Define Place value :

Place value is the basis of our entire number system. This is the system in which the position of a digit in a number determines its value. The number 42,316 is different from 61,432 because the digits are in different positions.

                     [or]

Place value of a digit can be defined as the position of the particular digit in a given number.

For Example :

The place value of 7 in 1870 is  70

Given the data,

First we use digit is :    184.36

Second we use digit is : 9027.83

Place value of 8 in First digit is  ⇒ 184.36 is 80

Place value of 8 in Second digit is ⇒ 9027.83  is 0. 8

We can solve the Ratio is :

                      Ratio = 80/ 0.8

                       Ratio = 100 times

Therefore,

Thus, the number with the larger value of 8 is 184.36 and it is 100 times larger.

Learn more about event correlation here: brainly.com/question/17103145

#SPJ9

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I hope this helps you



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6 0
3 years ago
A sample size 25 is picked up at random from a population which is normally
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Answer:

a) P(X < 99) = 0.2033.

b) P(98 < X < 100) = 0.4525

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}

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 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.

Mean of 100 and variance of 36.

This means that \mu = 100, \sigma = \sqrt{36} = 6

Sample of 25:

This means that n = 25, s = \frac{6}{\sqrt{25}} = 1.2

(a) P(X<99)

This is the pvalue of Z when X = 99. So

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

By the Central Limit Theorem

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

Z = \frac{99 - 100}{1.2}

Z = -0.83

Z = -0.83 has a pvalue of 0.2033. So

P(X < 99) = 0.2033.

b) P(98 < X < 100)

This is the pvalue of Z when X = 100 subtracted by the pvalue of Z when X = 98. So

X = 100

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

Z = \frac{100 - 100}{1.2}

Z = 0

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X = 98

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

Z = \frac{98 - 100}{1.2}

Z = -1.67

Z = -1.67 has a pvalue of 0.0475

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So

P(98 < X < 100) = 0.4525

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Parentheses

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