In a positive association, as one variable increases, the other variable also increases.
In a negative association, as one variable increases, the other variable decreases.
In a no association, there is no apparent relationship or correlation between the two variables.
Different types of association between variables
Positive Association: In a positive association, as one variable increases, the other variable also increases. This means that the two variables move in the same direction.
Real-world example: The relationship between studying hours and exam scores. Typically, if a student increases their studying hours, their exam scores tend to increase as well. The more time and effort a student puts into studying, the higher their chances of achieving better grades.
Negative Association: In a negative association, as one variable increases, the other variable decreases. This means that the two variables move in opposite directions. When graphed, a negative association forms a downward-sloping line.
Real-world example: The relationship between the price of a product and the quantity demanded. As the price of a product increases, the quantity demanded tends to decrease.
No Association: In a no association, there is no apparent relationship or correlation between the two variables. Changes in one variable do not consistently correspond to changes in the other variable. When graphed, a no association appears as scattered data points with no clear pattern or trend.
Real-world example: The relationship between shoe size and intelligence. There is no logical connection between shoe size and intelligence, and one's shoe size does not predict or influence their intelligence level. Therefore, there is no association between these two variables.