Difference between Z notation and sigma

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Homework Help Overview

The discussion revolves around the differences between Z notation and the notation used for expressing probabilities related to a normal distribution, specifically in the context of the statement involving P(|X - μ| ≤ 0.675σ). Participants explore the implications of algebraic manipulations and the properties of the normal distribution.

Discussion Character

  • Conceptual clarification, Mathematical reasoning, Assumption checking

Approaches and Questions Raised

  • Participants discuss the equivalence of different probability expressions and the algebraic steps involved in converting between them. Questions arise regarding the role of symmetry in the normal distribution and its relation to the algebraic manipulations. Some participants express confusion about specific notations and their meanings.

Discussion Status

The discussion is ongoing, with participants providing insights into the algebraic relationships and properties of the normal distribution. There is a mix of clarifications and questions, with no clear consensus reached on all points raised.

Contextual Notes

Some participants note the importance of understanding the properties of the normal distribution in relation to the algebraic expressions being discussed. There are indications of potential misunderstandings regarding notation and the implications of symmetry.

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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