Respuesta :
Answer:
For this case we define the dependent variable as Y and the independent variable X. We assume that we have n observations and that means the following pairs:
[tex] (x_1, y_1) ,....,(x_n,y_n)[/tex]
For this case we assume that we want to find a linear regression model given by:
[tex] \hat y = \hat m x +\hat b[/tex]
Where:
[tex] \hat m[/tex] represent the estimated slope for the model
[tex]\hat b[/tex] represent the estimated intercept for the model
And for any estimation of the dependent variable [tex]\hat y_i , i=1,...,n[/tex] is given by this model.
The difference between the observed value of the dependnet variable and the value predicted using the estimated regression equation is known as residual, and the residual is given by this formula:
[tex]e_i = y_i -\hat y_i , i=1,...,n[/tex]
So the best option for this case is:
d. residual
Step-by-step explanation:
For this case we define the dependent variable as Y and the independent variable X. We assume that we have n observations and that means the following pairs:
[tex] (x_1, y_1) ,....,(x_n,y_n)[/tex]
For this case we assume that we want to find a linear regression model given by:
[tex] \hat y = \hat m x +\hat b[/tex]
Where:
[tex] \hat m[/tex] represent the estimated slope for the model
[tex]\hat b[/tex] represent the estimated intercept for the model
And for any estimation of the dependent variable [tex]\hat y_i , i=1,...,n[/tex] is given by this model.
The difference between the observed value of the dependnet variable and the value predicted using the estimated regression equation is known as residual, and the residual is given by this formula:
[tex]e_i = y_i -\hat y_i , i=1,...,n[/tex]
So the best option for this case is:
d. residual
