What is the standard deviation for a rock dropped off a cliff?

In summary: So, if you want to find the standard deviation of the Gaussian distribution, you just plug in the standard deviation of the Gaussian distribution for <x> and <x^2> and get the standard deviation of the product, which is the same as the standard deviation of the original Gaussian distribution.In summary, the standard deviation of the distribution is 2h and the expectation value is h/3.
  • #1
Bill Foster
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Homework Statement



Problem 1.2 from the book Introduction to Quantum Mechanics (2e) by Griffiths:

Suppose a rock is dropped off a cliff of height h. As it

falls, a million photos are snapped, at random intervals. On each picture the distance the rock has fallen is measured.

a) Find the standard deviation of the distribution.

The Attempt at a Solution



Starting with the equation of motion (assuming it falls in the positive x direction):

[tex]x(t)=x_0+v_0(t)+\frac{1}{2}at^2=\frac{1}{2}gt^2[/tex]

The time T it takes to fall:

[tex]h=\frac{1}{2}gt^2[/tex]
[tex]T=\sqrt{\frac{2h}{g}}[/tex]

Probability a picture is taken in interval dt:

[tex]\frac{dt}{T}=dt \sqrt{\frac{g}{2h}}=\frac{dx}{gt}\sqrt{\frac{g}{2h}}=\frac{1}{2h}\frac{1}{\sqrt{gt}}dx=\frac{1}{2\sqrt{hx}}dx[/tex]

So the probability density is:

[tex]\rho(x)=\frac{1}{2\sqrt{hx}}[/tex]

Expectation Value:

[tex]\mu=\int_0^h x \rho(x)dx=\int_0^h x \frac{1}{2\sqrt{hx}}dx=\frac{1}{2\sqrt{h}}\frac{2}{3}x^\frac{3}{2}=\frac{h}{3}[/tex]

Standard deviation:

[tex]\sigma^2=\int_0^h (x-\mu)^2 \rho(x)dx[/tex]
[tex]\sigma^2=\int_0^h (x-\frac{h}{3})^2 \frac{1}{2\sqrt{hx}}dx=\frac{4}{45}h^2[/tex]
[tex]\sigma=\frac{2h}{\sqrt{45}}[/tex]

Look right?
 
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  • #2
I'm pretty sure that's right. How about this one:

Consider the Gaussian distribution [tex]\rho(x)=Ae^{-\lamba(x-a)^2}[/tex] where A, α, and λ are positive real constants.

Find <x>, <x²> and std dev.

[tex]<x>=\int_{-\infty}^{\infty} xAe^{-\lamba(x-a)^2}dx[/tex]

[tex]<x^2>=\int_{-\infty}^{\infty} x^2Ae^{-\lamba(x-a)^2}dx[/tex]

[tex]\sigma^2=<x^2>-<x>^2[/tex]

I tried using integration by parts to evaluate the integrals, but I didn't get anywhere. I also tried looking them up on a table of integrals - also no luck.

I'm guessing <x>=a because a is the peak in a Gaussian distribution. But how do I show it?
 
  • #3
For <x> just change variables to t=(x-a). If A is properly normalized, you should find <x>=a after you cancel the antisymmetric part of the integral. For <x^2> there's a standard trick to find this. You know a formula for the integral of exp(-k*x^2) from x=-infinity to infinity, right? Call it I(k). Then the integral of x^2*exp(-x^2) is closely related to d/dk(I(k)) evaluated at k=1. Isn't it?
 

1. What does standard deviation measure?

Standard deviation measures the amount of variation or dispersion in a set of data. It indicates how much the values in a dataset deviate from the mean value. A higher standard deviation means the data points are more spread out, while a lower standard deviation indicates that the data points are closer to the mean.

2. How is standard deviation calculated?

Standard deviation is calculated by finding the square root of the variance of a dataset. The variance is calculated by taking the sum of the squared differences between each data point and the mean, divided by the total number of data points. The square root of the variance gives the standard deviation.

3. What is a high or low standard deviation?

A high standard deviation indicates that the data points are more dispersed and further away from the mean, while a low standard deviation means that the data points are closer to the mean. A standard deviation of 0 means that all the data points are the same. The magnitude of the standard deviation value depends on the units of the data, so it is important to compare standard deviations within the same dataset and units.

4. Why is standard deviation important in data analysis?

Standard deviation is important because it provides a measure of the spread of data and helps identify outliers or extreme values. It also allows for comparison between different datasets and can help determine the reliability of the data. Standard deviation is used in many statistical analyses and is a fundamental concept in understanding the distribution of data.

5. Can standard deviation be negative?

No, standard deviation cannot be negative. It is always a positive value, as it is the square root of the variance, which is always positive. If the standard deviation is close to 0, it means that the data points are very close to the mean, but it cannot be negative.

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