Example of statistical relationship between Gender and Income

Let's conduct a survey and ask 20 fictional people about their Gender and Income, 10 males and 10 females:

We see that females earn more than males. The values of Income increase when the values of Gender change from Male to Female. Thus, there is a statistical relationship between Gender and Income. We could also calculate the 20 people's average Income by Gender and plot it in a bar chart:

This bar chart also shows that our females earn more than our males. Thus, there is a statistical relationship between Gender and Income.

Example of statistical relationship between Age and Income

Let's conduct a new survey. This time we ask 100 fictional people about their Age and Income:

We see that older people earn more than younger people. The values of Income increase when the values of Age change. Thus, there is a statistical relationship between Age and Income.

Example of no statistical relationship between Height and Income

We conduct yet another survey. This time we ask 150 fictional people about their Height and Income:

We see that tall people earn the same as shorter people. The values of Income doesn't increase or decrease when the values of Height change. A statistical relationship requires one variable's values to increase or decrease when the other variable's values change. Thus, there is no statistical relationship between Height and Income.

Similar terms and concepts

With regression analysis, you can analyze the statistical relationship between two or more variables, like the statistical relationship between Gender and Income above. However, in regression analysis, we often use the term “effect” instead of “relationship”, like the effect of Gender on Income.

Easystat makes it as easy as possible to identify statistical relationships in your data with regression analysis. Using statistical expert knowledge and artificial intelligence, Easystat automatically selects the best statistical methods for you.