# Calibration Curves: Correlation Coefficient

JoVE Core
Analytical Chemistry
Zum Anzeigen dieser Inhalte ist ein JoVE-Abonnement erforderlich.  Melden Sie sich an oder starten Sie Ihre kostenlose Testversion.
JoVE Core Analytical Chemistry
Calibration Curves: Correlation Coefficient

### Nächstes Video1.19: Correlation and Regression

A correlation coefficient is a statistical test to evaluate the degree and the direction of linear correlation between two variables.

The Pearson correlation coefficient, denoted by 'r,' is commonly used. Here, 'n' represents the total number of observations.

The value of 'r' calculated in a calibration curve ranges from −1 to +1. While a value closer to +1 indicates a stronger positive linear correlation between the two variables, a value closer to −1 infers a stronger negative linear correlation, and a zero value affirms no linear correlation.

The coefficient of determination denoted by 'r2' or 'R2' is a better statistical test that tells the reliability of the mathematical model in explaining the variation in the data.

The R2 value ranges from 0 to 1. A value of 0.999 indicates an excellent fit.

## Calibration Curves: Correlation Coefficient

In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the other increases, and vice versa. On the contrary, a negative correlation value indicates that as one variable increases, the other variable decreases, and vice versa. Squaring the correlation coefficient results in the coefficient of determination, denoted by 'r2' or 'R2'. This value ranges from 0 to 1. A value closer to 1, such as 0.999, indicates an excellent fit, whereas a value close to 0 indicates a poor fit.