- #1
CraigH
- 222
- 1
The variance is denoted by [itex]σ^{2}[/itex]
It is calculated with this equation:
[itex]σ^{2}=\frac{\sum^{N}_{i=1}(Xi-μ)^{2}}{N}[/itex]
Which makes sense. To calculate the average (deviation from the mean)^2 you need to sum up the (deviations from the mean)^2 and then divided by the number of deviations.
The reason that you use (deviation from the mean)^2 instead of just the deviation from the mean is so that positive and negative numbers do not cancel out.
However in my lecture slides the formula is given by:
[itex]σ^{2}=\sum^{N}_{i=1}(Xi-μ)^{2}P(Xi)[/itex]
Which is different from what I have previously learnt.
All I can presume is that [itex]\sum^{N}_{i=1} P(Xi) = \frac{1}{N}[/itex]
But why? What is P(Xi), and why is it used instead of just using N?
On the previous slide there is an equation for P(X) which I presume is something to do with it.
This equation is:
[itex]P(X)=\frac{1}{σ\sqrt{2∏}} *e^{-\frac{(x-μ)^2}{2σ^2}}[/itex]
https://en.wikipedia.org/wiki/Normal_distribution
Although this doesn't make sense anymore. I used to be able to understand standard deviation and variance as the equations were quite intuitive, but now it makes no sense at all.
It would probably help if I understood the normal distribution (Gaussian Distribution) equation. What does this equation mean? And why is it used in the calculation of variance?
Please help!
Thank you!
It is calculated with this equation:
[itex]σ^{2}=\frac{\sum^{N}_{i=1}(Xi-μ)^{2}}{N}[/itex]
Which makes sense. To calculate the average (deviation from the mean)^2 you need to sum up the (deviations from the mean)^2 and then divided by the number of deviations.
The reason that you use (deviation from the mean)^2 instead of just the deviation from the mean is so that positive and negative numbers do not cancel out.
However in my lecture slides the formula is given by:
[itex]σ^{2}=\sum^{N}_{i=1}(Xi-μ)^{2}P(Xi)[/itex]
Which is different from what I have previously learnt.
All I can presume is that [itex]\sum^{N}_{i=1} P(Xi) = \frac{1}{N}[/itex]
But why? What is P(Xi), and why is it used instead of just using N?
On the previous slide there is an equation for P(X) which I presume is something to do with it.
This equation is:
[itex]P(X)=\frac{1}{σ\sqrt{2∏}} *e^{-\frac{(x-μ)^2}{2σ^2}}[/itex]
https://en.wikipedia.org/wiki/Normal_distribution
Although this doesn't make sense anymore. I used to be able to understand standard deviation and variance as the equations were quite intuitive, but now it makes no sense at all.
It would probably help if I understood the normal distribution (Gaussian Distribution) equation. What does this equation mean? And why is it used in the calculation of variance?
Please help!
Thank you!
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