Inequalities in Normal Distributions

AI Thread Summary
P(-1<Z<1) equals 1-2P(Z>1) due to the symmetry of the standard normal distribution around the mean of 0. The total area under the normal curve is 1, and the cumulative distribution function F(Z) helps calculate probabilities. By recognizing that P(-Z<z<Z) can be expressed as F(Z) - F(-Z), and using the property F(-Z) = 1 - F(Z), the relationship is established. This leads to the conclusion that the probability of Z being between -1 and 1 is equivalent to the area not exceeding 1 minus the tails beyond 1. Understanding these properties is essential for effectively using the normal distribution table.
Cpt Qwark
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Homework Statement



How does P(-1<Z<1) equal to 1-2P(Z>1)?
(So you can find the values on the Normal Distribution Table)

Homework Equations

The Attempt at a Solution


I tried P(-1+1<Z+1<1+1) but ended up with P(1<Z+1<2).
 
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The normal distribution function is symmetric, f(z)=f(-z). The table shows the values of Cumulated Distribution Function F(Z) =P(-∞<z <Z). The probability P(Z<z<∞)=1-F(Z). Because of the symmetry, P(Z<z<∞)=P(-∞<z<-Z), F(-Z)=1-F(Z). The probability that the variable z is between -Z and Z is P(-Z&lt;z&lt;Z)=F(Z)-F(-Z) = F(Z)-(1-F(Z)).
 
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Cpt Qwark said:

Homework Statement



How does P(-1<Z<1) equal to 1-2P(Z>1)?
(So you can find the values on the Normal Distribution Table)

Homework Equations

The Attempt at a Solution


I tried P(-1+1<Z+1<1+1) but ended up with P(1<Z+1<2).
The standard normal curve is symmetrical about a mean value μ = 0 like this:

http://www.spiritsd.ca/curr_content/mathb30/data/les6/images/norm_percent.gif​

The normal curve is scaled such that the total area under the curve is 1.

Since you are trying to find P(-1 < Z < 1), don't you see how that's the same probability as 1 - 2P(Z>1)?
 
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