How to Derive Uncertainty from Mean and RMS?

In summary, the conversation is about determining the uncertainty of a function, F = os - ss, where os and ss are opposite-sign and same-sign binomial distributions. The equations for the mean and rms of the function have been found, and it is noted that the rms is simply the square root of the variance. The reader is looking for the square root of the variance and expresses gratitude if the rms equation provided is correct.
  • #1
penguindecay
26
0
Dear Reader,

Earlier I posted a topic on the uncertainty of a function that is F = os - ss , where os and ss are opposite-sign and same-sign, and are both binomial distributions. I want to know the uncertainty of my F,

I have found a equation for the mean, and rms of my function which are:

N(total) * (2*prob(os) -1 ) for the mean and

2 * SQRT { N(total) * prob(os) * (1 - prob(os)) } for the rms

where N(total) = os+ss

Is it possible to get an equation for the variance or uncertainty from the given information?

Thank you for reading

Kim
 
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  • #2
I am not sure what you are looking for. However rms is simply the square root of the variance.
 
  • #3
mathman said:
I am not sure what you are looking for. However rms is simply the square root of the variance.

I am looking for the square root of the variance, and if it is as you state the rms equation I have, then thank you.
 

FAQ: How to Derive Uncertainty from Mean and RMS?

1. What is the difference between mean and rms?

Mean, also known as average, is a measure of central tendency and is calculated by summing all the values in a data set and dividing by the total number of values. RMS (root mean square), on the other hand, is a measure of dispersion and is calculated by squaring all the values, finding the mean, and taking the square root of the result.

2. How does uncertainty from mean and rms affect data analysis?

Uncertainty from mean and rms refers to the degree of variation in a data set. It is important in data analysis because it provides information about the reliability of the data and helps in making accurate conclusions and predictions. A higher uncertainty means the data is more spread out, while a lower uncertainty means the data is more concentrated around the mean.

3. Can uncertainty from mean and rms be reduced?

Uncertainty from mean and rms is inherent in any data set and cannot be completely eliminated. However, it can be reduced by increasing the sample size, which leads to a more accurate estimation of the mean and rms. Additionally, using more precise measuring instruments and careful data collection can also help in reducing uncertainty.

4. How is uncertainty from mean and rms calculated?

Uncertainty from mean and rms is typically calculated by finding the standard deviation, which measures the spread of data around the mean, and the standard error, which estimates the uncertainty in the mean. The uncertainty from mean is the standard error, while the uncertainty from rms is the standard deviation.

5. What is the significance of uncertainty from mean and rms in scientific research?

Uncertainty from mean and rms is a crucial factor in scientific research as it allows for proper interpretation and evaluation of data. It helps in determining the accuracy and precision of measurements and in identifying any potential errors or biases in the data. Understanding and addressing uncertainty is essential in producing reliable and valid scientific results.

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