What is a conditional variable in statistics? In probability theory and statistics, a conditional variance is **the variance of a random variable given the value(s) of one or more other variables**. Particularly in econometrics, the conditional variance is also known as the scedastic function or skedastic function.

Correspondingly, What is the basic meaning of standard deviation?

A standard deviation (or σ) is **a measure of how dispersed the data is in relation to the mean**. Low standard deviation means data are clustered around the mean, and high standard deviation indicates data are more spread out.

Then, How do you find the conditional variance? Similar to the conditional expectation, we can define the conditional variance of X, Var(X|Y=y), which is the variance of X in the conditional space where we know Y=y. If we let μX|Y(y)=E[X|Y=y], then **Var(X|Y=y)=E[(X−μX|Y(y))2|Y=y]=∑xi∈RX(xi−μX|Y(y))2PX|Y(xi)**=E[X2|Y=y]−μX|Y(y)2.

In this manner, What is conditional and marginal distribution?

**A marginal distribution is the percentages out of totals**, and conditional distribution is the percentages out of some column. UPD: Marginal distribution is the probability distribution of the sums of rows or columns expressed as percentages out of grand total.

What is conditional normal distribution?

The conditional distribution of given knowledge of is a normal distribution with. **Mean = μ 1 + σ 12 σ 22 ( x 2 − μ 2 )** Variance = σ 11 − σ 12 2 σ 22.

## Related Question for What Is A Conditional Variable In Statistics?

**How do you calculate conditional expectation?**

The conditional expectation, E(X |Y = y), is a number depending on y. If Y has an influence on the value of X, then Y will have an influence on the average value of X. So, for example, we would expect E(X |Y = 2) to be different from E(X |Y = 3).

**What does conditional and unconditional mean?**

Breaking apart the word unconditional can help you remember its meaning. Combine the prefix un-, meaning “not,” with conditional, meaning "dependent on something else," and you get an adjective for something that holds true without any conditions attached.

**What is conditional variance Garch?**

A process, such as the GARCH processes, where the conditional mean is constant but the conditional variance is nonconstant is an example of an uncorrelated but dependent process. The dependence of the conditional variance on the past causes the process to be dependent.

**What is conditional correlation?**

We define event conditional correlation as the correlation of two variables X and Y conditionally to an event A and denote it ρXY |A. Consider the case where one is able to measure (X, Y ) only if a third random variable Z is large enough, say larger than a threshold z.

**What is the difference between marginal and conditional?**

Marginal probability is the probability of an event irrespective of the outcome of another variable. Conditional probability is the probability of one event occurring in the presence of a second event.

**How do you find the conditional distribution in statistics?**

First, to find the conditional distribution of X given a value of Y, we can think of fixing a row in Table 1 and dividing the values of the joint pmf in that row by the marginal pmf of Y for the corresponding value. For example, to find pX|Y(x|1), we divide each entry in the Y=1 row by pY(1)=1/2.

**Why is it called marginal distribution?**

A marginal distribution gets it's name because it appears in the margins of a probability distribution table. The distribution must be from bivariate data. Bivariate is just another way of saying “two variables,” like X and Y.

**How do you find conditional density?**

The conditional density function is f((x,y)|E)={f(x,y)/P(E)=2/π,if(x,y)∈E,0,if(x,y)∉E.

**How do you do conditional probability with a normal distribution?**

**What is conditional mean in regression?**

If you look at any textbook on linear regression, you will find that it says the following: “Linear regression estimates the conditional mean of the response variable.” This means that, for a given value of the predictor variable X , linear regression will give you the mean value of the response variable Y .

**Is conditional variance the same as volatility?**

The difference is clear. While the unconditional variance is just the standard measure of the variance, the conditional variance represents the measure of the uncertainty about a variable given a model and an information set . Conditional volatility is the volatility of a random variable given some extra information.

**What is unconditional standard deviation?**

For example, the unconditional standard deviation of ^{1}X is denoted std(^{1}X). Conditional on information available at time 0, it is denoted ^{0}std(^{1}X). For example, the unconditional standard deviation of ^{t}X can be written as either std(^{t}X) or ^{t}σ.

**What are conditional models?**

The conditional models provide a powerful approach for using fully specified probability functions to fit nonnormal longitudinal data. These models are preferable when the trajectory of nonlinear response outcomes is of primary interest (Zeger et al., 1988).

**What does a standard deviation of 1.2 mean?**

Rebecca. Hi Rebecca, If you have a collection of data from a Normal Distribution then approximately 66% of the data should fall within one standard deviation of the mean. For exmple if the mean is 6 and the standard deviation is 1.2 then approximately 66% of the data is between. 6 - 1.2 = 4.8.

**Is 5 a high standard deviation?**

5 = Very Good, 4 = Good, 3 = Average, 2 = Poor, 1 = Very Poor, The mean score is 2.8 and the standard deviation is 0.54.

**Is lower standard deviation better?**

A high standard deviation shows that the data is widely spread (less reliable) and a low standard deviation shows that the data are clustered closely around the mean (more reliable).

**What is the purpose of standard deviation?**

Standard deviation tells you how spread out the data is. It is a measure of how far each observed value is from the mean. In any distribution, about 95% of values will be within 2 standard deviations of the mean.

**What is the use of standard deviation in real life?**

You can also use standard deviation to compare two sets of data. For example, a weather reporter is analyzing the high temperature forecasted for two different cities. A low standard deviation would show a reliable weather forecast.

**What is a good standard deviation for diabetes?**

Dr. Hirsch suggests that diabetics should aim for an SD of one-third of their mean blood sugar. So, if your mean blood sugar were 120 mg/dl, you would want your standard deviation to be no more than 40 mg/dl, or one-third of the mean.

**Is Arch stationary?**

Along with the zero covariance and zero mean, this proves that the ARCH(1) process is stationary. So conditional variance is a combination of the unconditional variance, and the deviation of squared error from its average value. . In general, a GARCH(p,q) model includes p ARCH terms and q GARCH terms.

**Does conditioning reduce variance?**

Intuitively, it seems clear that conditioning should reduce variance through the interpretation of conditioning as providing information. By the "law of total probability", we know that var(Y)<E[var(Y|X)] and var(Y)<var[E(Y|X)].

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