Residuals vs fitted plots can be used to assess whether the four key assumptions for a simple linear regression have been met. Each of the plots below displays an instance where at least one of these assumptions may not have been met. For each plot, identify which assumption has been most violated, if any.

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Residuals vs fitted plots can be used to assess whether the four key assumptions for a simple linear regression have been met. Each of the plots below displays an instance where at least one of these assumptions may not have been met. For each plot, identify which assumption has been most violated, if any.

2.8
3.0
3.2
3.4
3.6
3.8
Fitted value
2.
? v 2.
-10
-5
5
10
A. The data does not appear to be from independent observations.
Fitted value
B. No assumptions have been violated.
C. The relationship between x and y cannot be assumed to be linear.
D. The variability of points around the least-squares line is not roughly constant.
? v 3.
-0.4
-0.2
0.0
0.2
0.4
Fitted value
Residual
-0.5
0'0
S'O
Transcribed Image Text:2.8 3.0 3.2 3.4 3.6 3.8 Fitted value 2. ? v 2. -10 -5 5 10 A. The data does not appear to be from independent observations. Fitted value B. No assumptions have been violated. C. The relationship between x and y cannot be assumed to be linear. D. The variability of points around the least-squares line is not roughly constant. ? v 3. -0.4 -0.2 0.0 0.2 0.4 Fitted value Residual -0.5 0'0 S'O
-0.4
-0.2
0.0
0.2
0.4
Fitted value
20
-10
-5
10
Fitted value
OL
Residual
4.
Transcribed Image Text:-0.4 -0.2 0.0 0.2 0.4 Fitted value 20 -10 -5 10 Fitted value OL Residual 4.
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