Solve for P(Z<#) and P(|Z|<#) in Simple Statistics Problem

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The discussion revolves around solving for specific values of '#' in the context of a normal distribution, focusing on the probabilities P(Z<#)=0.9 and P(|Z|<#)=0.9. Participants clarify that Z represents a standard normal variable, and the probabilities can be determined using a Z-table. For P(Z<#), the value can be found by looking up the corresponding Z-value and adding 0.5, while for P(|Z|<#), the Z-value must be multiplied by 2 after lookup. Understanding these calculations is essential for applying concepts of probability distributions effectively. The conversation emphasizes the importance of recognizing the normal distribution in these types of statistical problems.
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The question reads exactly as follows,

Find the appropriate values for #'s.

a) P(Z<#)=.9
b) P(|Z|<#)=.9


We are currently going over distributions, poissons, density functions and binomial stuff, someone referenced to Z-values from a ztable but i don't know if this is what the topic at hand is. Does anyone know what they are asking?
 
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I assume Z is some random variable. What is its probability distribution?
 
It looks to me like they are asking "for what value of "#" is the probability that z is less than # equal to .9?" and "for what value of "#" is the probability that |z| is less than # equal to .9?" The answer, of course, will depend on the probability distribution. Since you refer to "z values" and a "z table" I suspect you are talking about a "normal distribution". Here is a table for the normal distribution:
http://people.hofstra.edu/Stefan_Waner/realworld/normaltable.html

Notice that this gives the probabilty that z is between 0 and the given number. To find the probabilty that z is less than a number, look up the value and add 0.5. To find the probability that |z| is than than the number, look up the value and multiply by 2.
 
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