1.The function to fit a linear model is lm(), and that function (a) will generate much data that should be assigned to a

[answerhappygod]

1.The function to fit a linear model is lm(), and that
function
(a) will generate much data that should be assigned to a R
object.
(b) will display a blue line in a cloud of data.
(c) only works for two explanatory variables.
(d) All of these answers are correct.
2.The difference between the value a model predicts and what
is actually observed is called
(a) a residual
(b) the error
(c) sometimes denoted as e
(d) All of these answers are correct.
3.You are inspecting the output of the geom_point() layer.
What
you get is
(a) a scatter plot displaying the relationship between two
variable.
(b) is a cloud of point that are randomly positioned.
(c) not a good tool for visual inspection of data.
(d) All of these answers are correct.
4. The default blue line drawn with geom_smooth( model = ’lm’)
is
(a) the line of best fit.
(b) used to make predictions.
(c) a graphical representation of the linear model, lm.
(d) All of the above answers are correct.
(e) None of these answers are correct.