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Brrunno [24]
3 years ago
12

6(7k-10)=24 solve for k

Mathematics
1 answer:
UNO [17]3 years ago
7 0

Answer:

if you don't understand something tell me so i send you explain step by step

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Help plz it’s urgent
vfiekz [6]

Answer:

3. 18 + 18 + 7 + 7 = 50

4. 120 degrees

Step-by-step explanation:

7 0
3 years ago
Help me this and show your work
krek1111 [17]
First set 2n-1=4n+5 and solve for n. once you have n plug it back into each equation and then multiply those two together. Do the same to n-3 and 2n+1. Once you get the answer to both subtract them
6 0
3 years ago
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%.

7 0
3 years ago
If these numbers represent temperatures in degrees Celsius, the coldest is
myrzilka [38]

Answer:

-3

Step-by-step explanation:

it is the smallest number on a number line which is kind of what a thermometer is

5 0
3 years ago
Write 18/4 as a mixed number in simplest form
otez555 [7]

Answer:

4 1/2

Hope this helps

6 0
3 years ago
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