Independence
If the conditional probabilities for Y are the same for all values of X, then Y is said to be independent of X.
If X and Y are independent, knowing the value of X does not give us any information about the likely value for Y.
Independence implies that the sub-populations corresponding to different values of X all contain values of Y in the same proportions.
Work performance and weight
As an example of independence, we continue with the (artificial) example on the previous page. We now show the relationship between weight and work performance (as assessed by a supervisor). In this model, weight and performance are independent — knowing someone's weight gives no clues as to that person's ability to do their job.
Work performance | ||||
---|---|---|---|---|
Poor | Satisfactory | Above average | Marginal | |
Underweight | 0.0225 | 0.1125 | 0.0150 | 0.1500 |
Normal | 0.0825 | 0.4125 | 0.0550 | 0.5500 |
Overweight | 0.0300 | 0.1500 | 0.0200 | 0.2000 |
Obese | 0.0150 | 0.0750 | 0.0100 | 0.1000 |
Marginal | 0.1500 | 0.7500 | 0.1000 | 1.0000 |
For this model, the conditional probabilities for work performance, given weight, are:
Work performance | ||||
---|---|---|---|---|
Poor | Satisfactory | Above average | Total | |
Underweight | 0.15 | 0.75 | 0.10 | 1.0 |
Normal | 0.15 | 0.75 | 0.10 | 1.0 |
Overweight | 0.15 | 0.75 | 0.10 | 1.0 |
Obese | 0.15 | 0.75 | 0.10 | 1.0 |
The conditional probabilities are the same for each weight, so knowing that a student is, say, obese does not affect the probability of being rated as an above-average worker. The proportional Venn diagram has the form shown below.
Note that the Proportional Venn Diagram now consists of a grid of horizontal and vertical lines.
Mathematical definition of independence
If Y is independent of X, then:
Also, if Y is independent of X, then X is also independent of Y.
Since the conditional and marginal probabilities are equal if Y and X are independent, an equivalent definition of independence is:
X and Y are independent if pxy = px × py