Step-by-step explanation:
We need to understand what residual plot is?
A residual plot is a graph that shows the residuals on the vertical axis and the independent variable on the horizontal axis.
Hence, there are 2 cases related to the relation between residual plot and a linear model:
1. If the points in a residual plot are randomly dispersed around the horizontal axis => the linear model is an appropriate fit for the data
2. If the points in a residual plot has a pattern => nonlinear model is more appropriate or the line have a bad fit with the set of the data.
Hope it will find you well.
Answer:
For this case we assume that we have n pairs of observations [tex](x_1 ,y_1) ,...,(x_n, y_n)[/tex]. Ad we have a model in order to estimate the real values [tex]y_i[/tex] with a model. And the residuals are given by:
[tex] e_i =y_i-\hat y_i[/tex]
Then we find the residuals for the n observations.
After that we can create a plot of [tex](e_i , x_i) , i =1,2,...,n[/tex].
Andd after create this graph if we see no pattern in the graph we can conclude that the linear pattern
Step-by-step explanation:
For this case we assume that we have n pairs of observations [tex](x_1 ,y_1) ,...,(x_n, y_n)[/tex]. Ad we have a model in order to estimate the real values [tex]y_i[/tex] with a model. And the residuals are given by:
[tex] e_i =y_i-\hat y_i[/tex]
Then we find the residuals for the n observations.
After that we can create a plot of [tex](e_i , x_i) , i =1,2,...,n[/tex].
Andd after create this graph if we see no pattern in the graph we can conclude that the linear pattern
