What is the significance of variance and covariance equations?

In summary: The nth moment is just the inverse of the variance. It can provide a measure of how important a particular attribute is. In summary, variance is a measure of how spread out a random variable is, while covariance is a measure of how correlated two random variables are. The significance of moments is that they can be used to determine the importance of certain attributes.
  • #1
dexterdev
194
1
I have idea of physical signif of var(x),cov(x) but can't get derivations of equation.

Hi all,
I understood the facts that variance indicate the spread in random variable and covariance shows correlation between 2 r.v s etc. But I cannot imagine how we are arriving at their equations. Also what is the significance of nth moment etc?

TIA

-Devanand T
 
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  • #2


Hey dexterdev.

You can relate moments with the Fourier Transform and thus make an intuitive connection between the PDF and its frequency characteristics with moments (I'm not talking about central moments, just the standard ones).

With regards to the variance of multiple random variables, the real key to this is to look at it in the context of linear algebra with matrices rather than as an equation.

In multiple-dimensions, you have a covariance matrix and if you want to find the variance of a linear combination of variables, you are going to apply your covariance matrix to that vector just like you multiply a matrix and vector using Ax = b.

In the covariance instance, your x vector represents the vector that is a linear combination of the random variables (for example [3 4 5] would represent 3X1 + 4X2 + 5X3) and if A is the covariance matrix, then Var(X) = XAX^T where A is your covariance matrix and X is your vector of random variables.

If there is no covariance terms you get a diagonal matrix.

Now you must consider the nature of variance: it acts in some ways like a metric or norm and you are dealing with issues involving positive definite attributes and constraints that things like metrics and norms face when you look at them abstractly in higher dimensions.

These are the basics of the ideas but if you want more context you will have to dig deeper.
 
  • #3


dexterdev said:
Hi all,
I understood the facts that variance indicate the spread in random variable and covariance shows correlation between 2 r.v s etc. But I cannot imagine how we are arriving at their equations. Also what is the significance of nth moment etc?

TIA

-Devanand T

Variance is somewhat arbitrary. It works pretty well and is mathematically easy to work with.
 

Related to What is the significance of variance and covariance equations?

What is the significance of var(x)?

The variance of a variable, var(x), measures the spread or dispersion of the data points around the mean. It is an important measure in understanding the variability and uncertainty in a dataset.

What does cov(x) represent?

The covariance, cov(x), is a measure of the linear relationship between two variables. It indicates the direction and strength of the relationship between the two variables.

How do I calculate var(x) and cov(x)?

The formula for calculating the variance, var(x), is the sum of squared differences between each data point and the mean, divided by the number of data points. The formula for calculating the covariance, cov(x), is the sum of the products of the differences of each data point from their respective means for two variables, divided by the number of data points.

Why is it important to understand the significance of var(x) and cov(x)?

Understanding the significance of var(x) and cov(x) allows for a better interpretation and analysis of data. It can help identify patterns and trends, as well as provide insight into the relationships between variables.

What are some real-world applications of var(x) and cov(x)?

Var(x) and cov(x) are commonly used in fields such as statistics, economics, and finance to analyze data and make predictions. They are also used in machine learning and data science for feature selection and dimensionality reduction. Additionally, they are important in understanding risk and volatility in financial markets.

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