Suppose we have a dataset with stock prices (Y) on different time points (X). After performing a linear regression of Y on X, we get the following plot of residuals vs fitted values.

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Suppose we have a dataset with stock prices (Y) on different time points (X). After performing a linear
regression of Y on X, we get the following plot of residuals vs fitted values.
2500
3000
3500
fited values
Which of the following statements, in your opinion, are true? Select all that apply.
Choose as many as you like
A Linear regression is not appropriate as residuals are not
independent.
B Residuals might not be normally distributed, but they are
independent.
c Nonlinear regression with independent errors should be used for
this data.
D Linear regression gives us good results and there is no need to use
any other model.
000G
0000
-1000 0 1000
sjenpsa
Transcribed Image Text:Suppose we have a dataset with stock prices (Y) on different time points (X). After performing a linear regression of Y on X, we get the following plot of residuals vs fitted values. 2500 3000 3500 fited values Which of the following statements, in your opinion, are true? Select all that apply. Choose as many as you like A Linear regression is not appropriate as residuals are not independent. B Residuals might not be normally distributed, but they are independent. c Nonlinear regression with independent errors should be used for this data. D Linear regression gives us good results and there is no need to use any other model. 000G 0000 -1000 0 1000 sjenpsa
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