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What does it mean if the residual plot is random?
The residual plot shows a fairly random pattern – the first residual is positive, the next two are negative, the fourth is positive, and the last residual is negative. This random pattern indicates that a linear model provides a decent fit to the data.
What is an unusual observation?
Unusual observations (also called influential observations) are observations that have a disproportionate impact on a regression or ANOVA model. Unusual observations are important to identify because they can produce misleading results.
What happens to the random pattern of a residual plot?
The Answer: The observation’s residual stands apart from the basic random pattern of the rest of the residuals. The random pattern of the residual plot can even disappear if one outlier really deviates from the pattern of the rest of the data.
How does an outlier show up on a residual plot?
Note that the residuals “fan out” from left to right rather than exhibiting a consistent spread around the residual = 0 line. The residual vs. fits plot suggests that the error variances are not equal. How does an outlier show up on a residuals vs. fits plot?
How to check the reliability of a residual plot?
How to Check Residual Plots When looking at residual plots, you simply want to determine whether the residuals are consistent with random error. I’ll use an analogy of rolling a die. You shouldn’t be able to use one roll to predict the outcome of the next roll because it is supposed to be random.
What do you call an unusually large residual?
Unusually large residuals are called outliers or extreme values. Another common type of pattern in residuals is when we can predict the value of residuals based on the preceding values of residuals. This is known variously as autocorrelation, serial correlation, and serial dependence.