A positive association is a relationship between two phenomena whereby the presence or increase of one phenomenon corresponds with the presence or increase of the other. This relationship can be observed in various contexts, including statistics, psychology, and biology. In statistics, a positive association indicates that two variables tend to move in the same direction, with an increase in one variable leading to an increase in the other. In psychology, a positive association may suggest a relationship between a certain behavior and a positive outcome, such as improved mood or increased self-esteem. In biology, a positive association could exist between the presence of a specific gene and the expression of a particular trait.
Positive Association
In statistics, a positive association means that as the value of one variable increases, the value of the other variable also tends to increase. For example, there is a positive association between height and weight. As people get taller, they also tend to get heavier.
There are two main types of positive associations:
- Linear association: The relationship between the two variables is linear, meaning that the points on a scatterplot form a straight line.
- Curvilinear association: The relationship between the two variables is curvilinear, meaning that the points on a scatterplot form a curve.
The strength of a positive association can be measured using a correlation coefficient. The correlation coefficient ranges from -1 to 1. A correlation coefficient of 1 indicates a perfect positive association, while a correlation coefficient of -1 indicates a perfect negative association. A correlation coefficient of 0 indicates no association between the two variables.
Positive associations can be caused by a variety of factors, including:
- Common cause: Both variables are affected by a common third variable. For example, height and weight are both affected by genetics.
- Direct cause-and-effect: One variable directly causes the other variable to change. For example, studying for a test (variable 1) directly causes the test score (variable 2) to improve.
Positive associations can be used to make predictions about the future. For example, if you know that there is a positive association between height and weight, you can predict that a person who is tall will also tend to be heavy.
Example of a Positive Association
The following table shows the heights and weights of 10 people.
| Height (inches) | Weight (pounds) |
|---|---|
| 60 | 120 |
| 62 | 125 |
| 64 | 130 |
| 66 | 135 |
| 68 | 140 |
| 70 | 145 |
| 72 | 150 |
| 74 | 155 |
| 76 | 160 |
| 78 | 165 |
The scatterplot of the data shows a positive linear association between height and weight. As height increases, weight also tends to increase.
The correlation coefficient for the data is 0.95, which indicates a strong positive association between height and weight.
Question 1: What is the definition of a positive association?
Answer: A positive association is a relationship between two variables in which one variable increases as the other variable increases. For example, in a positive association between height and weight, a person with a greater height will likely have a greater weight.
Question 2: How does a positive association differ from a negative association?
Answer: A positive association is the opposite of a negative association. In a positive association, one variable increases as the other variable increases, while in a negative association, one variable increases as the other variable decreases.
Question 3: What is the statistical measure of the strength of a positive association?
Answer: The statistical measure of the strength of a positive association is the correlation coefficient. The correlation coefficient can range between -1 and 1, with a value of 0 indicating no association, a value of 1 indicating a perfect positive association, and a value of -1 indicating a perfect negative association.
Well folks, that’s all there is to a positive association! I hope you found this little read helpful. If you enjoyed this, make sure to visit our site again in the future, and feel free to check out our other educational articles. Thanks again for reading!