Cant get my head around gaussians, so easy aswell?

  • Thread starter physical101
  • Start date
  • Tags
    Head
In summary, the mean error in a normal distribution is typically assumed to be 0, and stationarity means there is no change in this error over time.
  • #1
physical101
47
0
Hi there was wondering if some one could help me?
I think i understand what a gaussian is, its a normal distirbution where the distribution of data symmetrically tails off from the mean in both directions. I have been looking at FTIR error analysis and have constantly read that error is normally distributed. I have thought that this means that the majority of error introduced in the analysis is distributed around the mean.

I have also read that we should consider the distribution to stationary, and I have come to think of this as meaning that for for each signal the noise is independant.

My question is when viewing the normal distribution of error for stationary error points in books the mean is always zero.

Do i take this too mean that the mean is actually a stable error value and that the + and - points of inflection are deviations away from this stable value. So does this mean that in any normal distributions of error that the mean will always be 0?

So confused and would really appreciate it if any can help, i know its a bit basic for this site

thanks
 
Mathematics news on Phys.org
  • #2
The mean error being zero is an assumption about the overall bias in a linear system: if y = A + B x + u and a and b are the estimated values for A and B (estimated from data), then E[y] = E[a] + E x + E. If E[a] = A (meaning a is an unbiased estimator of A), E = B (b is an unbiased estimator of B), and I want E[y] = y, then E = 0 has to be the case.

Stationarity means that E is not changing over time, so there's no upward or downward drift (in an expectational or distributional sense) between any two time periods.
 

FAQ: Cant get my head around gaussians, so easy aswell?

What are gaussians and why are they important in science?

Gaussians, also known as Gaussian distributions or normal distributions, are a type of probability distribution that is commonly used in statistics and data analysis. They are important because they accurately describe many natural phenomena and can be used to make predictions and draw conclusions from data.

How do gaussians work and what do they represent?

Gaussians work by showing the probability of a random variable falling within a certain range of values. They represent a bell-shaped curve, with the highest probability at the center and lower probabilities on either side. The curve is symmetrical, with the mean, median, and mode all being the same value.

What is the formula for a gaussian distribution?

The formula for a gaussian distribution is:
f(x) = (1/(σ√2π))e^(-((x-μ)^2/(2σ^2)))
where μ is the mean and σ is the standard deviation of the distribution.

How are gaussians used in real-world applications?

Gaussians are used in a wide range of fields, including physics, biology, economics, and engineering. They are used to model and analyze data in various experiments and studies, as well as to make predictions and decisions based on data.

How can I better understand gaussians and their applications?

To better understand gaussians, it is important to have a basic understanding of statistics and probability. It can also be helpful to visualize the distribution using graphs and to practice with different examples and data sets. Seeking out additional resources and guidance from experts in the field can also aid in understanding gaussians and their applications.

Back
Top