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Strong positive correlation: When the value of one variable increases, the value of the other variable increases in a similar fashion. For example, the more hours that a student studies, the higher their exam score tends to be. Hours studied and exam scores have a strong positive correlation.
Understanding the basics of correlation, the brilliant theories behind it, and its practical use-cases lays a strong foundation for exploring the myriad examples of positive correlation in our world.
The strength of a positive correlation can range from 0 (no correlation) to +1 (perfect positive correlation). This valuable metric has many practical applications, from economics and healthcare to social science.
The correlation between the height of an individual and their weight tends to be positive. In other words, individuals who are taller also tend to weigh more. If we created a scatterplot of height vs. weight, it may look something like this: Example 2: Temperature vs. Ice Cream Sales.
A strong negative correlation, on the other hand, indicates a strong connection between the two variables, but that one goes up whenever the other one goes down. For example, a correlation of -0.97 is a strong negative correlation, whereas a correlation of 0.10 indicates a weak positive correlation.
A positive correlation is a relationship between two variables in which both variables move in the same direction. Therefore, one variable increases as the other variable increases, or one variable decreases while the other decreases. An example of a positive correlation would be height and weight. Taller people tend to be heavier. A negative ...
The Pearson correlation coefficient (r) is the most common way of measuring a linear correlation. It is a number between –1 and 1 that measures the strength and direction of the relationship between two variables. When one variable changes, the other variable changes in the same direction.
A positive correlation means that both variables change in the same direction. A negative correlation means that the variables change in opposite directions. A zero correlation means there’s no relationship between the variables.
Some real-life examples of positive correlations include: Number of study hours and test results: The more hours someone spends studying for an exam, the higher their test score is expected to be (better result). Exercise quantity and general fitness: An individual becomes more fit (health would improve) the more they exercise (more activity).
Height and Weight: Taller people generally weigh more than shorter people. Age and Healthcare Costs: As people age, they typically incur higher healthcare costs. Speed and Travel Time: Faster travel speeds usually result in shorter travel times.