Respuesta :
Answer:
Option B) Y - estimated average predicted value, X – predictor, Y-intercept ([tex]b_0[/tex]), slope ([tex]b_1[/tex])
Step-by-step explanation:
In the simple linear regression model estimate is given by:
[tex]Y = b_0 + b_1X[/tex]
Comparing this regression line with the slope intercept form, we get:
[tex]y = mx + c[/tex]
where m is the slope of the straight line and c is the y-intercept that is the value of y when x is zero.
here,
Y is the predicted variable or the variable we need to predict
X is the predictor or the variable that helps us to predict Y
[tex]b_1[/tex] is the slope of the regression line.
[tex]b_0[/tex] is the y-intercept or the value of y when x is zero.
Thus, Option B)
Y - estimated average predicted value, X – predictor, Y-intercept ([tex]b_0[/tex]), slope ([tex]b_1[/tex])
