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What does it mean for two variables to be independent in statistics?
The first component is the definition: Two variables are independent when the distribution of one does not depend on the the other. If the probabilities of one variable remains fixed, regardless of whether we condition on another variable, then the two variables are independent.
What is the concept of random variable?
A random variable is a variable whose value is unknown or a function that assigns values to each of an experiment’s outcomes. A random variable can be either discrete (having specific values) or continuous (any value in a continuous range).
How do you know when a variable is independent or dependent?
An easy way to think of independent and dependent variables is, when you’re conducting an experiment, the independent variable is what you change, and the dependent variable is what changes because of that. You can also think of the independent variable as the cause and the dependent variable as the effect.
How do I test if two random variables are independent?
You can tell if two random variables are independent by looking at their individual probabilities. If those probabilities don’t change when the events meet, then those variables are independent. Another way of saying this is that if the two variables are correlated, then they are not independent.
What are those random variables’ distributions?
The probability distribution for a random variable describes how the probabilities are distributed over the values of the random variable. For a discrete random variable, x, the probability distribution is defined by a probability mass function, denoted by f ( x ).
Are X and Y independent?
Thus, X and Y are not independent, or in other words, X and Y are dependent. This should make sense given the definition of X and Y. The winnings earned depend on the number of heads obtained. So the probabilities assigned to the values of Y will be affected by the values of X.
What are the properties of variance?
Basic Properties of the Variance. One useful result about variances which is relatively easy to show is that because the variance gives a measure or the square of the width of a distribution, the variance of a constant times a random variable is the square of the constant times the variance of the random variable.