The correlation between two variables is considered to be strong if the absolute value of r is greater than 0.75. The following table shows the rule of thumb for interpreting the strength of the relationship between two variables based on the value of r: Absolute value of r Chicken age and egg production have a strong negative correlation. For example, the older a chicken becomes, the less eggs they tend to produce. Strong negative correlation: When the value of one variable increases, the value of the other variable tends to decrease. Hours studied and exam scores have a strong positive correlation. For example, the more hours that a student studies, the higher their exam score tends to be. Strong positive correlation: When the value of one variable increases, the value of the other variable increases in a similar fashion. It’s important to note that two variables could have a strong positive correlation or a strong negative correlation. The further away r is from zero, the stronger the relationship between the two variables. Often denoted as r, this number helps us understand how strong a relationship is between two variables. 1 indicates a perfectly positive linear correlation between two variables.0 indicates no linear correlation between two variables.-1 indicates a perfectly negative linear correlation between two variables.In statistics, one of the most common ways that we quantify a relationship between two variables is by using the Pearson correlation coefficient, which is a measure of the linear association between two variables. In each of these scenarios, we’re trying to understand the relationship between two different variables. What is the relationship between marketing dollars spent and total income earned for a certain business?.What is the relationship between the temperature outside and the number of ice cream cones that a food truck sells?.What is the relationship between the number of hours a student studies and the exam score they receive?.

In statistics, we’re often interested in understanding how two variables are related to each other.

Strong negative correlation examples