A negative correlation can indicate a strong relationship or a weak relationship. Many people think that a correlation of –1 indicates no relationship. But the opposite is true. The minus sign simply indicates that the line slopes downwards, and it is a negative relationship.

The coefficient of friction is dimensionless and it does not have any unit. It is a scalar, meaning the direction of the force does not affect the physical quantity. The coefficient of friction depends on the objects that are causing friction. The value is usually between 0 and 1 but can be greater than 1.

When ρ is +1, it signifies that the two variables being compared have a perfect positive relationship; when one variable moves higher or lower, the other variable moves in the same direction with the same magnitude. The closer the value of ρ is to +1, the stronger the linear relationship.

Understanding Positive Correlation A perfectly positive correlation means that 100% of the time, the variables in question move together by the exact same percentage and direction. A positive correlation can be seen between the demand for a product and the product’s associated price.

Correlation coefficients whose magnitude are between 0.5 and 0.7 indicate variables which can be considered moderately correlated. Correlation coefficients whose magnitude are between 0.3 and 0.5 indicate variables which have a low correlation.

Correlation ranges from -1 to +1. Values closer to zero means there is no linear trend between the two variables. The close to 1 the correlation is the more positively correlated they are; that is as one increases so does the other and the closer to 1 the stronger this relationship is.

Regression analysis refers to assessing the relationship between the outcome variable and one or more variables. For example, a correlation of r = 0.8 indicates a positive and strong association among two variables, while a correlation of r = -0.3 shows a negative and weak association.

Correlation is a single statistic, or data point, whereas regression is the entire equation with all of the data points that are represented with a line. Correlation shows the relationship between the two variables, while regression allows us to see how one affects the other.

Difference Between Correlation And Regression

Both quantify the direction and strength of the relationship between two numeric variables. When the correlation (r) is negative, the regression slope (b) will be negative. When the correlation is positive, the regression slope will be positive.