statistical regression (Meaning)
statistical regression (n)
the relation between selected values of x and observed values of y (from which the most probable value of y can be predicted for any value of x)
Synonyms & Antonyms of statistical regression
No Synonyms and anytonyms found
statistical regression Sentence Examples
- Statistical regression is a technique used in statistics to analyze the relationship between a dependent variable and one or more independent variables.
- The statistical regression equation can help predict the value of the dependent variable based on the values of the independent variables.
- Linear statistical regression assumes that the relationship between the dependent and independent variables is linear.
- Nonlinear statistical regression models allow for more complex relationships between the variables.
- Multiple statistical regression models are used to analyze the relationship between a dependent variable and multiple independent variables.
- Statistical regression analysis can be used to make predictions about future outcomes based on historical data.
- The strength of the statistical regression model is determined by the correlation coefficient, which indicates the degree of association between the variables.
- Statistical regression is a powerful tool for understanding the relationships between variables and making predictions.
- Statistical regression can be used in various fields, such as economics, psychology, and medicine.
- It is important to note that statistical regression does not imply causation, but rather shows the correlation between variables.
FAQs About the word statistical regression
the relation between selected values of x and observed values of y (from which the most probable value of y can be predicted for any value of x)
No synonyms found.
No antonyms found.
Statistical regression is a technique used in statistics to analyze the relationship between a dependent variable and one or more independent variables.
The statistical regression equation can help predict the value of the dependent variable based on the values of the independent variables.
Linear statistical regression assumes that the relationship between the dependent and independent variables is linear.
Nonlinear statistical regression models allow for more complex relationships between the variables.
