multiple correlation
- Noun:
- A statistical measure of association: "Multiple correlation" is a statistical technique that quantifies the strength of the linear relationship between one dependent variable and a set of two or more independent variables considered together.
- A predictive modeling concept: It refers to the process of predicting the value of a single outcome variable based on the known values of several predictor variables.
- "Multiple correlation" is used primarily in the fields of statistics, econometrics, psychology, and data science. It is the foundation for multiple regression analysis.
- The result is expressed as the multiple correlation coefficient, often denoted by the capital letter R. This coefficient measures how well the combination of independent variables predicts the dependent variable.
- Noun:
- The researcher calculated the multiple correlation to determine how well age and income predicted spending habits.
- A high multiple correlation coefficient indicates that the set of predictors does a good job of forecasting the outcome variable.
"Multiple correlation analysis": The complete process of investigating and modeling the relationship between multiple independent variables and one dependent variable.
- The study employed multiple correlation analysis to assess the combined effect of diet, exercise, and genetics on heart health.
"Squared multiple correlation (R²)": Represents the proportion of variance in the dependent variable that is predictable from the independent variables.
- An R² of 0.75 means that 75% of the variance in the outcome can be explained by the multiple correlation with the predictors.
Multiple regression (n): The broader analytical method of which multiple correlation is a core part. Regression provides the equation for prediction, while correlation measures the strength of the relationship.
- They used multiple regression to create a predictive model.
Multiple correlation coefficient (R) (n): The specific numerical measure, ranging from 0 to 1, resulting from a multiple correlation analysis.
- The multiple correlation coefficient for the model was 0.89.
Partial correlation (n): A related technique that measures the relationship between two variables while controlling for the effect of one or more other variables.
- Multivariate correlation: A more general term often used interchangeably, though it can sometimes refer to correlations among several sets of variables.
- Coefficient of multiple determination (R²): Specifically refers to the squared value of the multiple correlation coefficient.
- Predictor variables: The independent variables used in the analysis.
- Criterion variable: The dependent or outcome variable being predicted.
- Linear relationship: The type of association assumed between the variables in standard multiple correlation.
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a statistical technique that predicts values of one variable on the basis of two or more other variables
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