Finding standard deviation or error from normalized data.

In summary, to find the standard deviation or error in normalized sets of data, you can multiply each datum in the data set by a scaling factor and then calculate the standard deviation using the formula: |k| times the standard deviation of the original data.
  • #1
doublemint
141
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Hello All,

I am trying to figure out how to find the standard deviation or error in sets of data. So let's say I have sets x1, x2, x3 and x4 with various values and I found the average and standard deviations for it. Now I have to take the averages, let's say a1, a2, a3, a4, and normalize a2,a3,a4 to a1. Now how do I find the standard deviation or error in the normalized sets? Forgive my ignorance, but I am suppose to do this for a project and I have never taken any stats course before..

Thanks
DoubleMint
 
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  • #2
What do you mean by "normalize"? For example, do you mean that multiply each datum in the data set x2 by the factor (a2/a1) ?

Let the data be the [itex] d_i [/itex]. Let the sample mean be [itex] m[/itex] . Let the scaling factor be [itex] k [/itex]

The mean of the scaled data [itex] k d_i [/itex] is [itex] m k [/itex]

The variance of the scaled data is:

[itex] \sum \frac { ( k d_i - m k )^2}{n} = \sum \frac{k^2 (d_i - k)^2 }{n} = k^2 \sum \frac{(d_i - m)^2}{n} [/itex]

This is [itex] k^2 [/itex] times the variance of the original sample.

So the sample standard deviation of the scaled data is [itex] |k| [/itex] times the standard deviation of the original data.
 

What is the formula for finding standard deviation from normalized data?

The formula for finding standard deviation from normalized data is:
Standard Deviation = √(∑(normalized data - mean)^2 / n), where n is the total number of data points.

How do you calculate the standard error from normalized data?

To calculate the standard error from normalized data, you can use the formula:
Standard Error = Standard Deviation / √n, where n is the total number of data points.

Why is it important to normalize data before calculating standard deviation or error?

Normalizing data helps to remove the effect of different scales and units on the data, making it easier to compare and analyze. This ensures that the calculated standard deviation or error accurately represents the variability or uncertainty of the data.

Can standard deviation or error be negative?

No, standard deviation and standard error cannot be negative as they represent a measure of spread and cannot have a negative value. If your calculated value is negative, it may indicate an error in your calculation.

How can I interpret the standard deviation or error from normalized data?

Standard deviation and standard error both provide a measure of the variability or uncertainty of the data. A higher value indicates a larger spread or uncertainty, while a lower value indicates a smaller spread or uncertainty. It is important to consider the context of the data and the purpose of the analysis when interpreting these values.

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