How to Handle Non-Normal Residuals in Multiple Regression Analysis

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In summary: After that, you can try the regression again.If that first plot is a QQ plot with reference to a standardized normal, I think your assertion will be wrong. But if you are looking at the plot to check the assumptions, then it would be okay.If you are trying to solve a real life problem, you should consider the realities of the problem and reveal them to your would-be advisors. For example, what are the physics? Is there some physical law that predicts the curve from which you compute the residuals? Is the precision of your measuring equipment limited to a certain number of digits? There aren't any mathematical laws that can specify a correct answer to your question if it is abstracted to
  • #1
bradyj7
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Hi there,

I have performed a multiple regression analysis and I am checking the assumptions. The normal probability plot is not exactly a straight line, but is it okay? If you were looking at the plot would you be satisfied hat the normality assumption is valid? https://dl.dropbox.com/u/54057365/All/residuLPLOT.jpg

I performed an Anderson Darling test on the residuals and the residuals were found not to be normal. What would you do in this case? Would you transform some of the predictors? Would you transform them all or just some?

I'd appreciate any comments.

Thanks

John
 
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  • #2
If you are trying to solve a real life problem, you should consider the realities of the problem and reveal them to your would-be advisors. For example, what are the physics? Is there some physical law that predicts the curve from which you compute the residuals? Is the precision of your measuring equipment limited to a certain number of digits?

There aren't any mathematical laws that can specify a correct answer to your question if it is abstracted to a few graphs. (Of course, there can be lots of suggestions about how to make the graphs look different. I'm just saying that there aren't mathematical theorems that prove one way is better than another.)
 
  • #3
Last not least there are statistical test to decide whether to accept normality or not. I guess they would reject normality in your case. The question is whether this is of any relevance for your problem.
 
  • #4
DrDu said:
Last not least there are statistical test to decide whether to accept normality or not. I guess they would reject normality in your case. The question is whether this is of any relevance for your problem.

If that first plot is a QQ plot with reference to a standardized normal, I think your assertion will be wrong.

But OP, as others have said, you need to put this into the context of the problem and the data and what both of them actually relate to physically, experimentally, and intuitively.
 
  • #6
Thank you for suggestions. Much appreciated.
 
  • #7
not normal. perform some transformation such as log, reciprocal etc to the variable.
 
  • #8
bradyj7 said:
Hi there,

I have performed a multiple regression analysis and I am checking the assumptions. The normal probability plot is not exactly a straight line, but is it okay? If you were looking at the plot would you be satisfied hat the normality assumption is valid?


https://dl.dropbox.com/u/54057365/All/residuLPLOT.jpg

I performed an Anderson Darling test on the residuals and the residuals were found not to be normal. What would you do in this case? Would you transform some of the predictors? Would you transform them all or just some?

I'd appreciate any comments.

Thanks

John

The residuals are skewed, so they aren't normal. The mode should be around zero but it is actually -0.8.
 
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  • #9
You can transform dependent variable.

But there is another possibility about data problem. There may be some extreme outliers in the data. Try to remove or adjust the outlier first by some appropriate method such as "Winsoriztion".
 

What does it mean for residuals to be normal?

When we say that residuals are normal, we are referring to the assumption that the errors in a statistical model follow a normal distribution. This means that the majority of the residuals will be clustered around the mean, with fewer residuals occurring at the extremes.

How do I know if my residuals are normal?

The most common way to check for normality is by creating a histogram of the residuals and visually inspecting it. If the histogram appears to be bell-shaped and symmetric, then the residuals are likely normal. Additionally, you can perform a formal statistical test, such as the Shapiro-Wilk test, to determine if the residuals are normally distributed.

Why is it important for residuals to be normal?

Normality of residuals is an important assumption in many statistical models, such as linear regression. If the residuals are not normally distributed, it can impact the accuracy and validity of the model's results. It is also important for making valid inferences and predictions based on the model.

What should I do if my residuals are not normal?

If the residuals are not normal, there are a few potential solutions. One option is to transform the data using a mathematical function, such as logarithmic or square root transformation, to make the residuals more normally distributed. Another option is to use a different statistical model that does not rely on the assumption of normality, such as a generalized linear model.

Can I still trust my results if my residuals are not normal?

The impact of non-normal residuals on the validity of your results depends on the severity of the deviation from normality and the specific model being used. In some cases, the results may still be reliable and meaningful even with non-normal residuals. It is important to consult with a statistician to determine the best course of action for your particular situation.

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