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Mazyrski [523]
3 years ago
5

Formula of Santander deviation .​

Mathematics
2 answers:
joja [24]3 years ago
4 0

Answer:

Step-by-step explanation:

kolbaska11 [484]3 years ago
4 0

Answer:

Work out the Mean(the simple average of the numbers)

Then for each number: subtract the Mean and square the result.

Then work out the Mean of those squared differences.

Take the square root of that and we are done!

Step-by-step explanation:

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375g of flour

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Rockwell hardness of pins of a certain type is known to have a mean value of 50 and a standard deviation of 1.8. (Round your ans
Alenkinab [10]

Answer:

a) 0.011 = 1.1% probability that the sample mean hardness for a random sample of 17 pins is at least 51

b) 0.0001 = 0.1% probability that the sample mean hardness for a random sample of 45 pins is at least 51

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}}.

For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.

In this problem, we have that:

\mu = 50, \sigma = 1.8

(a) If the distribution is normal, what is the probability that the sample mean hardness for a random sample of 17 pins is at least 51?

Here n = 17, s = \frac{1.8}{\sqrt{17}} = 0.4366

This probability is 1 subtracted by the pvalue of Z when X = 51. So

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

By the Central Limit Theorem

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

Z = \frac{51 - 50}{0.4366}

Z = 2.29

Z = 2.29 has a pvalue of 0.9890

1 - 0.989 = 0.011

0.011 = 1.1% probability that the sample mean hardness for a random sample of 17 pins is at least 51

(b) What is the (approximate) probability that the sample mean hardness for a random sample of 45 pins is at least 51?

Here n = 17, s = \frac{1.8}{\sqrt{45}} = 0.2683

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

Z = \frac{51 - 50}{0.0.2683}

Z = 3.73

Z = 3.73 has a pvalue of 0.9999

1 - 0.9999 = 0.0001

0.0001 = 0.1% probability that the sample mean hardness for a random sample of 45 pins is at least 51

8 0
3 years ago
If (a+bi)^2=2abi, what must be the relationship between a and b? Show or explain your reasoning.
Klio2033 [76]
(a+bi)^2 =(a+bi)(a+bi)= a^2 +2abi+b^2 i^2 \\  \\ i^2 = -1 \\  \\ (a+bi)^2 = a^2-b^2 +2abi = 2abi \\  \\ a^2 -b^2 = 0 \\  \\ a^2 = b^2 \\  \\ a = b

For the statement to be true, 'a' and 'b' must be equal

*First expand the binomial using FOIL, then set it equal to whats given "2abi".
Then you can find the relationship between a and b.
6 0
3 years ago
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