# Variance and mean relationship

### Mean and Variance of Random Variables

where the sum is taken over all possible values of X. E(X) is also called the mean of X or the average of X, because it represents the long-run average value if. Mean and Variance of Random Variables. Mean. The mean of a discrete random variable X is a weighted average of the possible values that the random. where ymax is the maximum of the sample, A is the arithmetic mean, H is the.

We can compare the distribution to a mass distribution, by thinking of the class marks as point masses on a wire the x-axis and the relative frequencies as the masses of these points. In this analogy, the mean is literally the center of mass--the balance point of the wire.

Recall also that we can think of the relative frequency distribution as the probability distribution of a random variable X that gives the mark of the class containing a randomly chosen value from the data set. With this interpretation, the mean of the frequency distribution is the same as the mean or expected value of X. Variance and Standard Deviation The variance of a data set is the arithmetic average of the squared differences between the values and the mean.

Again, when we summarize a data set in a frequency distribution, we are approximating the data set by "rounding" each value in a given class to the class mark. Thus, the variance of a frequency distribution is given by The standard deviation is the square root of the variance: The variance and the standard deviation are both measures of the spread of the distribution about the mean.

The variance is the nicer of the two measures of spread from a mathematical point of view, but as you can see from the algebraic formula, the physical unit of the variance is the square of the physical unit of the data. For example, if our variable represents the weight of a person in pounds, the variance measures spread about the mean in squared pounds.

- Standard Deviation and Variance (1 of 2)
- Moments: Mean and Variance

On the other hand, standard deviation measures spread in the same physical unit as the original data, but because of the square root, is not as nice mathematically. Both measures of spread are useful.

Again we can think of the relative frequency distribution as the probability distribution of a random variable X that gives the mark of the class containing a randomly chosen value from the data set.

### Expectation and Variance – Mathematics A-Level Revision

With this interpretation, the variance and standard deviation of the frequency distribution are the same as the variance and standard deviation of X. The Applet As before, you can construct a frequency distribution and histogram for a continuous variable x by clicking on the horizontal axis from 0. You can select class width 0.

The mean, variance, and standard deviation are recorded numerically in the second table. The mean and standard deviation are shown graphically as the horizontal red bar below the x-axis. This bar is centered at the mean and extends one standard deviation on either side.

In the applet, set the class width to 0. Compute the min, max, mean, variance, and standard deviation by hand, and verify that you get the same results as the applet.

Then increase the class width to each of the other four values.

### Mean, Variance, and Standard Deviation

A unimodal distribution that is skewed right. For a continuous random variable, the mean is defined by the density curve of the distribution. For a symmetric density curve, such as the normal densitythe mean lies at the center of the curve. The law of large numbers states that the observed random mean from an increasingly large number of observations of a random variable will always approach the distribution mean.

## Mean and Variance of Random Variables

That is, as the number of observations increases, the mean of these observations will become closer and closer to the true mean of the random variable. This does not imply, however, that short term averages will reflect the mean. Unfortunately for her, this logic has no basis in probability theory. The law of large numbers does not apply for a short string of events, and her chances of winning the next game are no better than if she had won the previous game.

Properties of Means If a random variable X is adjusted by multiplying by the value b and adding the value a, then the mean is affected as follows: The new probability distribution for each outcome is provided by the following table: With the new payouts, the casino can expect to win 20 cents in the long run.

Suppose that the casino decides that the game does not have an impressive enough top prize with the lower payouts, and decides to double all of the prizes, as follows: This is equivalent to multiplying the previous value of the mean by 2, increasing the expected winnings of the casino to 40 cents.

**Gamma Mean, Variance, and Chi Squared**

Overall, the difference between the original value of the mean 0. The mean of the sum of two random variables X and Y is the sum of their means: Variance The variance of a discrete random variable X measures the spread, or variability, of the distribution, and is defined by The standard deviation is the square root of the variance.

Example In the original gambling game above, the probability distribution was defined to be: