Helpp, since this question is a bit hard, ill give brainliest and 20 pts.

Mathematics

1 answer:

Morgarella [4.7K]4 years ago

5 0

Step-by-step explanation:

A. When dealing with large numbers, sometimes it's easier to write them in a form of exponents of number ten. The value of the exponent shows how many times we multiply 10 by itself. That means that 10^3 is 10•10•10 or that 10^6 is 10•10•10•10•10•10.

So, when finding how many times on number is greater then the other, we need to divide them. We divide 8x10^6 by 2x10^5. It is done by dividing the numbers and subtracting the exponents; 8/2=4 and 10^6/10^5 is 10^6-5=10^1. So the correct answer is 4x10^1 which is 4x10, and that is 40.

B. Now, we have a total number of coins (2.25x10^5), and diametar of a coin (19mm = 19x10^-6km). Our task is to calculate the distance across which the coiks laid side-by-side would expand. We can find this by multiplying the number of coins with a diametar of single penny, 2.25x10^5•19x10^-6. Multiplying is done by multiplying the numbers (2.25x19=42.75) and adding the exponents (5+(-6)=5-6=-1). So, the distance is 42.75x10^-1km, which equals to 4.275 km. Obviously, this is less then the stated 5 km given in the text, so the reportet's statement is false.

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6x+4y=12 into y=my+b

kirza4 [7]

6x + 4y = 12...subtract 6x from both sides
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(4/4)y = (-6/4)x + 12/4...reduce
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7 0

3 years ago

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The mean rent of a 3-bedroom apartment in Orlando is $1300. You randomly select 10 apartments around town. The rents are normall

lana [24]

Answer:

96.49% probability that the mean rent is more than $1100

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