
What is the formula for the variance of a probability distribution?
Answer
446.1k+ views
Hint: We know the variance of a random variable X is the mean or expected value of the squared deviation from the mean of X. Using this definition, we can arrive at a simpler formula for variance, both for continuous and discrete random variables.
Complete step-by-step answer:
We know that the variance measures how far a set of numbers is spread out, from their average value. Also, we are aware that variance is the square of standard deviation. The variance of a random variable X is represented by or Var(X).
Using the definition of variance, for a random variable X, we can write the variance as
where, E(X) represents the expected value or mean for the random variable X.
We can expand the above equation as
Hence, we can write
Thus, we can simplify this to get
Hence, we can also say that the variance of X is equal to the difference of the mean of the square of X and the square of the mean of X.
We know that for a continuous random variable X, the total probability is
.
Also, we can write the expected value as,
.
Hence, we can write the variance using equation (i) as
.
Similarly, for a discrete random variable X, we have the total probability as,
.
Also, we can write the expectation of X as,
.
Thus, we can write the variance using equation (i) as
.
Note: We must take care that in the above explanation, we have assumed that the probability distribution of continuous random variable to be defined from a to b, and the discrete random variable is assumed to have n different possibilities.
Complete step-by-step answer:
We know that the variance measures how far a set of numbers is spread out, from their average value. Also, we are aware that variance is the square of standard deviation. The variance of a random variable X is represented by
Using the definition of variance, for a random variable X, we can write the variance as
where, E(X) represents the expected value or mean for the random variable X.
We can expand the above equation as
Hence, we can write
Thus, we can simplify this to get
Hence, we can also say that the variance of X is equal to the difference of the mean of the square of X and the square of the mean of X.
We know that for a continuous random variable X, the total probability is
Also, we can write the expected value as,
Hence, we can write the variance using equation (i) as
Similarly, for a discrete random variable X, we have the total probability as,
Also, we can write the expectation of X as,
Thus, we can write the variance using equation (i) as
Note: We must take care that in the above explanation, we have assumed that the probability distribution of continuous random variable to be defined from a to b, and the discrete random variable is assumed to have n different possibilities.
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