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skad [1K]
2 years ago
15

When solving 389 : 6=, what is the remainder 4 5 6 2

Mathematics
1 answer:
kenny6666 [7]2 years ago
6 0
The answer is 64 with a remainder of 5
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A cylinder has a volume of 90pi and a radius of 3. what is the height?
kykrilka [37]

Step-by-step explanation:

  • volume of cylinder=90π
  • πr²h=90π
  • 3²h=90
  • 9h=90
  • h=90/9
  • h=10

hope it helps.

8 0
2 years ago
11, 16, 21, ...<br> Find the 44th term.<br> PLZ HELP
VashaNatasha [74]

answer : 226

difference : 5

‘0th’ term : 6

rule : 5n + 6

if n = 44

220 + 6 = 226

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3 years ago
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What is three-fourths of 1,968
lara31 [8.8K]
All you have to so is divide 1968 by 4.

1968 ÷ 4 = 496.5 = 1/4


Now multiply 496.5 by 3 =  1, 489.5 = 3/4


I hope this helps! :_
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3 years ago
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The graph of the equation x - y = 4
Sophie [7]
The answer should be A
5 0
3 years ago
The Office of Student Services at a large western state university maintains information on the study habits of its full-time st
Vera_Pavlovna [14]

Answer:

0.8254 = 82.54% probability that the mean of this sample is between 19.25 hours and 21.0 hours

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 20 hours, standard deviation of 6:

This means that \mu = 20, \sigma = 6

Sample of 150:

This means that n = 150, s = \frac{6}{\sqrt{150}}

What is the probability that the mean of this sample is between 19.25 hours and 21.0 hours?

This is the p-value of Z when X = 21 subtracted by the p-value of Z when X = 19.5. So

X = 21

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

By the Central Limit Theorem

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

Z = \frac{21 - 20}{\frac{6}{\sqrt{150}}}

Z = 2.04

Z = 2.04 has a p-value of 0.9793

X = 19.5

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

Z = \frac{19.5 - 20}{\frac{6}{\sqrt{150}}}

Z = -1.02

Z = -1.02 has a p-value of 0.1539

0.9793 - 0.1539 = 0.8254

0.8254 = 82.54% probability that the mean of this sample is between 19.25 hours and 21.0 hours

3 0
3 years ago
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