regression of y on x Synonyms
No Synonyms and anytonyms found
regression of y on x Meaning
regression of y on x (n)
the equation representing the relation between selected values of one variable (x) and observed values of the other (y); it permits the prediction of the most probable values of y
regression of y on x Sentence Examples
- The regression of y on x revealed a strong negative correlation between the two variables.
- The regression equation for y on x indicates that y decreases by 2 units for every 1 unit increase in x.
- The coefficient of determination for the regression of y on x is 0.95, suggesting that 95% of the variation in y can be explained by the variation in x.
- The residual plot for the regression of y on x shows a random distribution of points, indicating that the model is an appropriate fit for the data.
- The regression of y on x with x2 as a quadratic term revealed a parabolic relationship between the variables.
- The regression of y on x with log(x) as an independent variable produced a linear relationship between the transformed variables.
- The multiple regression of y on x1, x2, and x3 indicated that x1 and x3 had significant effects on y, while x2 did not.
- The stepwise regression of y on x1, x2, x3, and x4 identified x1 and x3 as the most important predictors of y.
- The regression tree for y on x1, x2, and x3 split the data into distinct groups with different regression lines.
- The elastic net regression of y on x1, x2, and x3 produced a model with both L1 and L2 regularization, reducing overfitting and improving predictive performance.
FAQs About the word regression of y on x
the equation representing the relation between selected values of one variable (x) and observed values of the other (y); it permits the prediction of the most p
No synonyms found.
No antonyms found.
The regression of y on x revealed a strong negative correlation between the two variables.
The regression equation for y on x indicates that y decreases by 2 units for every 1 unit increase in x.
The coefficient of determination for the regression of y on x is 0.95, suggesting that 95% of the variation in y can be explained by the variation in x.
The residual plot for the regression of y on x shows a random distribution of points, indicating that the model is an appropriate fit for the data.
