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ziro4ka [17]
3 years ago
8

What is 4,321,109,432 rounded to the nearest ten million

Mathematics
2 answers:
Anarel [89]3 years ago
8 0
 4,320,000,000.
Reason:
1. First fine the ten million place.....
2. Look at the next number to the right (3)....
3. If the number is greater than 5 you round up one which is not so you dont round.....
5. Which keeps you at 4,320,000,000.....
If you need anymore help feel free to ask me!
Hope this helps!
Brook aka SlayQueen~
SOVA2 [1]3 years ago
5 0
To round to the nearest ten million, you look at the millions digit. In this case, the millions digit is 1, which is less than 5, so you round the number down to 4,320,000,000.
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Tom bought 3 packages of Skittles for $1.25 each. How much did he spend?
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Tom spent $3.75 of Skittles

Step-by-step explanation:

If each Skittle packet was $1.25, then if that is multiplied by 3, the product of the equation is, 3 dollars and 75 cents/$3.75

1.25 x 3 = 3.75

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Suppose that in one region of the country the mean amount of credit card debt perhousehold in households having credit card debt
kvv77 [185]

Answer:

The probability that the mean amount of credit card debt in a sample of 1600 such households will be within $300 of the population mean is roughly 0.907 = 90.7%.

Step-by-step explanation:

To solve this question, we have 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 random variable X, with mean \mu and standard deviation \sigma, a large sample size can be approximated to a normal distribution with mean \mu and standard deviation s = \frac{\sigma}{\sqrt{n}}

In this problem, we have that:

\mu = 15250, \sigma = 7125, n = 1600, s = \frac{7125}{\sqrt{1600}} = 178.125

The probability that the mean amount of credit card debt in a sample of 1600 such households will be within $300 of the population mean is roughly

This probability is the pvalue of Z when X = 1600 + 300 = 1900 subtracted by the pvalue of Z when X = 1600 - 300 = 1300. So

X = 1900

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

By the Central Limit Theorem

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

Z = \frac{1900 - 1600}{178.125}

Z = 1.68

Z = 1.68 has a pvalue of 0.9535.

X = 1300

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

Z = \frac{1300 - 1600}{178.125}

Z = -1.68

Z = -1.68 has a pvalue of 0.0465.

0.9535 - 0.0465 = 0.907.

The probability that the mean amount of credit card debt in a sample of 1600 such households will be within $300 of the population mean is roughly 0.907 = 90.7%.

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