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A conditional distribution is a distribution of values for one variable that exists when you specify the values of other variables. This type of distribution allows you to assess the dispersal of your variable of interest under specific conditions, hence the name.

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 pXY(x1), we divide each entry in the Y=1 row by pY(1)=1/2.

The conditional distributions can be graphically compared using side by side bar graphs of one variable for each value of the other variable. Here, the percents are calculated by age range (columns).

Conditional probability refers to the chances that some outcome occurs given that another event has also occurred. It is often stated as the probability of B given A and is written as P(BA), where the probability of B depends on that of A happening.

Conditional distribution. And this is the distribution of one variable given something true about the other variable. So, for example, an example of a conditional distribution would be the distribution of percent correct given that students study between, let's say, 41 and 60 minutes.

A conditional distribution is a distribution of values for one variable that exists when you specify the values of other variables. This type of distribution allows you to assess the dispersal of your variable of interest under specific conditions, hence the name.

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 pXY(x1), we divide each entry in the Y=1 row by pY(1)=1/2.