Normal Distribution with Non-Standard Mean and Variance: Solving for P(X>1)

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SUMMARY

The discussion focuses on calculating probabilities using the normal distribution when the mean and variance are not standard. Specifically, it addresses the problem of finding P(X > 1) for a random variable X that follows the distribution N(2, 3). The solution involves transforming the variable using the formula z = (x - mu) / sigma, which converts X into a standard normal variable z that follows N(0, 1). By manipulating the inequality, the probability can be expressed in terms of z, allowing for the use of standard normal distribution tables to find the desired probability.

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Homework Statement



how to do with the normal distribution if the mean is not 0 variance is not 1.
for example p(x>1) if x-N(2,3) ?

Homework Equations





The Attempt at a Solution

 
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You use a transformation of your random variable X, namely z = (x - mu)/sigma. Here z is N(0, 1).
The transformation works the other way, too, with x = z sigma + mu.
The probability you want is
P(x > 1)
= P(z sigma + mu > 1)
Work with the expression in parentheses above, using the properties of inequalities, to get a probability involving z all by itself. The idea is that if you have equivalent inequalities, the probabilities will be equal. You should end up with a probability of the form P(z > ...). You can find that probability in a table of standard normal distribution probabilities.
 

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