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## What is the law of total probability for variances?

For any two random variables X and Y , we find Var(X) = E(Var(X|Y )) + Var(E(X|Y )). Since variances are always non-negative, the law of total variance implies Var(X) ≥ Var(E(X|Y )).

**How do you use the law of total expectations?**

Law of Total Expectation When Y is a discrete random variable, the Law becomes: The intuition behind this formula is that in order to calculate E(X), one can break the space of X with respect to Y, then take a weighted average of E(X|Y=y) with the probability of (Y = y) as the weights.

**What is total variance of data?**

The total variance is the sum of variances of all individual principal components. The fraction of variance explained by a principal component is the ratio between the variance of that principal component and the total variance. For several principal components, add up their variances and divide by the total variance.

### What does higher variance mean?

Variance measures how far a set of data is spread out. A variance of zero indicates that all of the data values are identical. A high variance indicates that the data points are very spread out from the mean, and from one another. Variance is the average of the squared distances from each point to the mean.

**What is total variance explained in factor analysis?**

The Total column gives the eigenvalue, or amount of variance in the original variables accounted for by each component. The \% of Variance column gives the ratio, expressed as a percentage, of the variance accounted for by each component to the total variance in all of the variables.

**How do you find the total variance?**

How to Calculate Variance

- Find the mean of the data set. Add all data values and divide by the sample size n.
- Find the squared difference from the mean for each data value. Subtract the mean from each data value and square the result.
- Find the sum of all the squared differences.
- Calculate the variance.

#### What is the expectation of variance?

Given a random variable, we often compute the expectation and variance, two important summary statistics. The expectation describes the average value and the variance describes the spread (amount of variability) around the expectation.

**What is the law of expectation?**

The law of expectation basically says you’re never going to get more than what you expect out of life. If you expect small things, you’re going to get small things. If you expect big things, you’re more likely to get big things.

**How do you explain total variance?**

## What is variance explained variance?

Explained variance (also called explained variation) is used to measure the discrepancy between a model and actual data. In other words, it’s the part of the model’s total variance that is explained by factors that are actually present and isn’t due to error variance.

**What is the law of total variance?**

Law of total variance. Jump to navigation Jump to search. In probability theory, the law of total variance or variance decomposition formula or conditional variance formulas or Law of Iterated Variances also known as Eve’s law, states that if X and Y are random variables on the same probability space, and the variance of Y is finite, then.

**What is Eve’s law of total variance?**

In probability theory, the law of total variance or variance decomposition formula or conditional variance formulas or law of iterated variances also known as Eve’s law, states that if X and Y are random variables on the same probability space, and the variance of Y is finite, then

### What does the variance tell us?

This equation tells us that the variance is a quantity that measures how much the r. v. X is spread around its mean. Simply put, the variance is the average of how much X deviates from its mean. Since it is the expectation of a squared quantity, the variance is always positive:

**What is the $Var(Y|X)$ law?**

This law is assuming that you are “breaking up” the sample space for $Y$ based on the values of some other random variable $X$. In this context, both $Var (Y|X)$ and $E [Y|X]$ are random variables.