Assumptions of Pearson Correlation Coefficient
- Linear Relationship: Karl Pearson’s correlation coefficient assumes a linear relationship between the two variables under consideration. It implies that as one variable changes, the other changes proportionally.
- Normality: The variables should follow a normal distribution. While Pearson’s correlation coefficient is robust to deviations from normality, extreme departures may affect the validity of the correlation analysis.
- Homoscedasticity: This assumption suggests that the variability in one variable should be consistent across all levels of the other variable. In other words, the spread of data points around the regression line should remain constant.
- Interval or Ratio Scale: Pearson’s correlation coefficient is appropriate for variables measured on an interval or ratio scale. These scales ensure meaningful numerical distances between observations.
- Independence: The observations used to compute the correlation coefficient should be independent of each other. Independence ensures that each data point contributes uniquely to the analysis without being influenced by other observations.
Pearson Correlation Coefficient
Pearson Correlation Coefficient: Correlation coefficients are used to measure how strong a relationship is between two variables. There are different types of formulas to get a correlation coefficient, one of the most popular is Pearson’s correlation (also known as Pearson’s r) which is commonly used for linear regression.
The Pearson correlation coefficient, often symbolized as (r), is a widely used metric for assessing linear relationships between two variables. It yields a value ranging from –1 to 1, indicating both the magnitude and direction of the correlation. A change in one variable is mirrored by a corresponding change in the other variable in the same direction.
This article provides detailed information on the Pearson Correlation Coefficient, its meaning, formula, interpretation, examples, and FAQs.
Table of Content
- What is the Pearson Correlation Coefficient?
- Pearson’s Correlation Coefficient Formula
- Pearson Correlation Coefficient Table
- Pearson Correlation Coefficient Origin
- Types of Pearson Correlation Coefficient
- Adjusted Correlation Coefficient
- Weighted Correlation Coefficient
- Reflective Correlation Coefficient
- Scaled Correlation Coefficient
- Pearson’s Distance
- Circular Correlation Coefficient
- Partial Correlation
- Pearson Correlation Coefficient Interpretation
- Finding the Correlation Coefficient with Pearson Correlation Coefficient Formula
- Assumptions of Pearson Correlation Coefficient
- Correlation Coefficient Properties
- Pearson Correlation Coefficient Interpretation
- Bivariate Correlation
- Correlation Matrix
- Pearson Correlation Coefficient Examples
- Pearson Correlation Coefficient Practice Problems
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