Solving Variance Problem - Hi All

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In summary, the conversation discusses modifying a dataset V with a size of n*1 to have a new variance of var(V_hat)=y. The person first calculates the error that needs to be spread on each element in the dataset, but this approach does not give the desired result. Another person suggests multiplying each element of V by sqrt(y/x) as a simple and effective solution.
  • #1
Asuralm
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Hi all:
If I have a dataset V of the size n*1. Assume that the mean of the dataset is 0 and var(V)=x is its variance. If I want to modify this dataset so that the variance of the new dataset will be var(V_hat)=y. The errors are spreaded average on each element in the dataset. What I did is first calculate the error which need to be speaded on each elements

v_hat - v = delta = sqrt[(y-x)*(n-1) / n] (0)

because

var(V) = x = sum(v^2)/(n-1) (1)
var(V_hat) = y = sum(v_hat^2)/(n-1) (2)

(2)-(1) and rearrange I got the equation (0);

However, this didn't give me the supposed answer. Could anyone point out what's the error in this or if there is any better methods please?
Thanks a lot!
 
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  • #2
I am not sure what limits you have on the modification. However, you can multiply each element of V by sqrt(y/x) to get what you want.
 
  • #3
What a nice and easy way. How stupid I am. Thanks a lot!
 

FAQ: Solving Variance Problem - Hi All

What is a variance problem?

A variance problem is a statistical concept that refers to the spread or variability of a set of data points around the mean value. It measures how far each data point is from the average and can help identify patterns or trends in the data.

How do you solve a variance problem?

To solve a variance problem, you first need to calculate the mean of the data set. Then, for each data point, you subtract the mean from the data point and square the result. Next, you add up all the squared differences and divide by the total number of data points. This will give you the variance of the data set.

Why is solving variance problems important?

Solving variance problems is important because it can help us understand the distribution of data and identify any outliers or unusual patterns. It is also used in many statistical analyses to determine the reliability and accuracy of data.

What are some common methods for reducing variance?

There are several methods for reducing variance, including increasing sample size, controlling for variables, and using more precise measurement techniques. Other techniques include transforming the data and using statistical tests to identify and remove outliers.

How can I apply solving variance problems in my research or work?

Solving variance problems can be applied in various fields such as economics, finance, psychology, and biology, to name a few. It can help you understand the data you are working with and make more accurate interpretations and predictions. It is also a useful tool for identifying and addressing any issues or anomalies in your data set.

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