Total movement of bacteria assuming a random distribution

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fatpotato
Homework Statement
Find an expression for the total movement ##s## of a bacteria knowing that its movement follow a normal distribution.
Relevant Equations
Expected value ##\bar{x} = \int_{-\infty}^{\infty}x\cdot p(x)dx##
Gaussian function ##p(x) = \frac{1}{\sqrt{2\pi}} \exp{-\frac{x^2}{2}}##
Hello,

I have to find an expression for the total movement of a bacteria ##s##, knowing that the bacteria is placed (centered) on a two side ruler at position ##x=0## (so a negative ##x## value means the bacteria has moved to the left of the ruler) and that the probability it moves to ##x## is given by the Gaussian function :
$$p(x) = \frac{1}{\sqrt{2\pi}} \exp{-\frac{x^2}{2}}$$
I have to express the total displacement ##s## using the fact that ##\bar{x} = \int_{-\infty}^{\infty}x\cdot p(x) dx## is the average expected displacement. Of course, since ##p(x)## is centered, ##\bar{x}## equals to zero, which makes sense both mathematically and physically, but now I have trouble finding a connection between this and a total displacement.

I thought about using ##s = \int_{-a}^{a}x\cdot p(x) dx## where ##a## would be the total displacement at point ##a##, but obviously, since ##x\cdot p(x)## is odd, any integral of this form will yield zero, so I thought about taking only one side with ##s = \int_{0}^{a}x\cdot p(x) dx##, yet I am not convinced.

Evaluating this new integral gives the following result :
$$s = \int_{0}^{a}x\cdot p(x) dx = \frac{1}{\sqrt{2\pi}} \big( 1 - \exp{-\frac{a^2}{2}} \big)$$
This means that the total displacement lies in the interval ##[0; \frac{1}{\sqrt{2\pi}})##, but why would be the total displacement of ##s## anything other than ##s## itself? My technique would imply that if we want to look at the total displacement ##s## of a bacteria, we have to first look at "all the movements up to point ##a##".

Does this make any sense?

Thanks in advance.

Edit : Relevant equations formatting
 
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Don't you need time t as parameter for dispersion, e.g. 
[tex]P(x,t) =\frac{N}{2\sqrt{\pi}Dt}exp(-\frac{x^2}{4Dt})[/tex]?

As for limited range integration, may I interpret it that we would omit active bacteria that go beyond this border ?
 
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anuttarasammyak said:
Don't you need time t as parameter for dispersion, e.g. 
[tex]P(x,t) =\frac{N}{2\sqrt{\pi}Dt}exp(-\frac{x^2}{4Dt})[/tex]?
In this exercise, time is not considered at all (not even mentionned!).

Out of curiosity, is your equation used to model dispersion? What are ##N## and ##D## ?
 
Thanks for your curiosity. N is source or number of participating bacteria. D is dispersion coefficient expressing activeness of bacteria.

As you said <x>=0. Possible candidates of your interest are
[tex]<|x|>=\int_{-\infty}^{+\infty}|x|P(x) dx= 2\int_0^{+\infty} x P(x)dx[/tex]
[tex]<x^2>=\int_{-\infty}^{+\infty} x^2 P(x)dx[/tex]
[tex]\sqrt{<x^2>}=\sqrt {\int_{-\infty}^{+\infty} x^2 P(x)dx}[/tex]
 
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Even if time is not explicitly mentioned in the problem, I would interpret it at meaning that the bacteria moves a distance [itex]X \sim N(0,\sigma^2)[/itex] in one time unit, so that its total distance moved after [itex]n[/itex] time units is the sum of [itex]n[/itex] independent identically dstributed normal random variables.
 
anuttarasammyak said:
As for limited range integration, may I interpret it that we would omit active bacteria that go beyond this border ?
I have trouble finding meaning myself. I suppose this could be a valid interpretation, although I don't know what my instructor has in mind.

anuttarasammyak said:
Possible candidates of your interest are
Alas, we are not supposed to know about these equations (which are second order moment and RMS value if I am not mistaken), only the expected value equation has to be used.

pasmith said:
Even if time is not explicitly mentioned in the problem, I would interpret it at meaning that the bacteria moves a distance [itex]X \sim N(0,\sigma^2)[/itex] in one time unit, so that its total distance moved after [itex]n[/itex] time units is the sum of [itex]n[/itex] independent identically dstributed normal random variables.
Would this translate to the equation I have written in my first post?