linear regression Antonyms
No Synonyms and anytonyms found
Meaning of linear regression
linear regression (n)
the relation between variables when the regression equation is linear: e.g., y = ax + b
linear regression Sentence Examples
- Linear regression is a statistical technique used to determine the relationship between a single dependent variable and one or more independent variables.
- The linear regression line represents the best-fit straight line that minimizes the sum of the squared deviations from the data points.
- The slope of the linear regression line indicates the rate of change in the dependent variable for each unit change in the independent variable.
- The intercept of the linear regression line represents the predicted value of the dependent variable when the independent variable is zero.
- Linear regression models can be used for both prediction and explanation.
- Linear regression analysis involves estimating the coefficients of the model, which represent the strength and direction of the relationship between the variables.
- Regularization techniques can be applied to linear regression models to reduce overfitting and improve prediction accuracy.
- Linear regression is a powerful tool for understanding the relationships between variables, but it is important to consider the assumptions and limitations of the model.
- The coefficient of determination, R-squared, measures the proportion of the variance in the dependent variable that is explained by the linear regression model.
- Linear regression can be extended to multiple linear regression, which allows for the modeling of multiple independent variables simultaneously.
FAQs About the word linear regression
the relation between variables when the regression equation is linear: e.g., y = ax + b
No synonyms found.
No antonyms found.
Linear regression is a statistical technique used to determine the relationship between a single dependent variable and one or more independent variables.
The linear regression line represents the best-fit straight line that minimizes the sum of the squared deviations from the data points.
The slope of the linear regression line indicates the rate of change in the dependent variable for each unit change in the independent variable.
The intercept of the linear regression line represents the predicted value of the dependent variable when the independent variable is zero.
