Difference between Z notation and sigma

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The discussion focuses on the equivalence between expressions in z-notation and standard notation for probabilities involving a normal distribution. It clarifies that converting between these notations involves algebraic manipulation rather than properties specific to the normal distribution. The participants highlight that the symmetry of the normal distribution does not affect the algebraic steps taken, as similar manipulations apply to any distribution. The conversation also touches on the significance of absolute value in these transformations. Overall, the thread emphasizes understanding the mathematical principles behind these notations rather than relying solely on distribution-specific characteristics.
Ry122
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Homework Statement


What is the difference between this statement being expressed in z-notation and the notation shown? What are the benefits of the two and how do you do the conversion?
Nevermind about answering the question itself.

if X ~N(mu,sigma) show that P(|X-mu|<= 0.675*sigma) = 0.5

Homework Equations

The Attempt at a Solution

 
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Ry122 said:

Homework Statement


What is the difference between this statement being expressed in z-notation and the notation shown? What are the benefits of the two and how do you do the conversion?
Nevermind about answering the question itself.

if X ~N(mu,sigma) show that P(|X-mu|<= 0.675*sigma) = 0.5

Homework Equations

The Attempt at a Solution

The statement that$$
P(|X-\mu|\le .675\sigma)=.5$$is completely equivalent to$$
P\left(\frac{|X-\mu|}{\sigma}\le .675\right)=.5$$which is equivalent to$$
P\left(-.675\le \frac{X-\mu}{\sigma}\le .675\right)=.5$$Is that what you are asking?
 
is that due to symmetry?
 
Ry122 said:
is that due to symmetry?

No, it is just algebra. To get the second statement you just divide both sides of the inequality by the positive ##\sigma## and the last form just uses properties of absolute values, specifically that ##|x|\le a## is the same thing as ##-a\le x \le a##.
 
The only normal distribution whose cdf and inverse cdf you will find tabulated is N(0,1). If X \sim N(\mu, \sigma^2) then to use tables you have instead to work with Z = (X - \mu)/\sigma \sim N(0,1).
 
what is function N()?
 
Ry122 said:
what is function N()?

It is exactly the same N( ) that you yourself wrote in post #1. It is actually not a function, but, rather, a name (short for "Normal" or "Normal distribution").
 
LCKurtz said:
No, it is just algebra. To get the second statement you just divide both sides of the inequality by the positive ##\sigma## and the last form just uses properties of absolute values, specifically that ##|x|\le a## is the same thing as ##-a\le x \le a##.

True, but doesn't the symmetry in the algebra follow from the symmetry of the normal distribution?
 
Last edited:
WWGD said:
True, but doesn't the symmetry in the algebra follows from the symmetry of the normal distribution?

No:. For a general density we have
P(|X-\mu| \leq a) = P(-a \leq X- \mu \leq a) = \int_{\mu-a}^{\mu+a} f(x) \, dx,
whether or not ##f## possesses any symmetry properties.
 
  • #10
I don't think so. There's absolutely no information about the normal distribution that goes into the algebraic manipulations LCKurtz showed above. The same algebra would have been used if X obeyed a completely different distribution.
 
  • #11
What I meant is that the original formula, in the OP is satisfied by the normal, i.e. ##, P((|X- \mu|)/\sigma <0.675) ## reflects the symmetry of the normal. Obviously, ##|x|< a \rightarrow -a<x<a ## follows straightforward from definition of absolute value. But I guess the OP was referring to the derivation ##P(|x- \mu|/\sigma< 0.675 \rightarrow -0.675 \sigma < P(|x-\mu| )< 0.675 \sigma ##, so my bad, I jumped in without reading carefully.
 
  • #12
WWGD said:
True, but doesn't the symmetry in the algebra follow from the symmetry of the normal distribution?
It follows from the "symmetry" in the absolute value.
 
  • #13
HallsofIvy said:
It follows from the "symmetry" in the absolute value.

Please check my last post, since I corrected myself. I was referring to something different.
 
  • #14
WWGD said:
##P(|x- \mu|/\sigma< 0.675 \rightarrow -0.675 \sigma < P(|x-\mu| )< 0.675 \sigma ##

Why would that hold? What does ##\mathbb{P}(|X-\mu)|)## even mean?
 
  • #15
micromass said:
Why would that hold? What does ##\mathbb{P}(|X-\mu)|)## even mean?

##\mathb {P}(|x- \mu|)## doesn't mean anything, what I wrote was ##P(|x-\mu|<\sigma (.675))## , which means ##P( -0.675 \sigma < x- \mu < 0.675\sigma ) ##. Anyway, I jumped in without reading carefully and I wrote something that is pointless here.

All I was trying to say (replying to no one but my own head) was that , by symmetry of the normal, ##P( x-\mu< \sigma) =P(x-\mu > -\sigma )## , which is not true for all distributions.
 
Last edited:

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