Linear and Logistic Regression are some of the most common techniques applied in data analysis. Here is a list of possible problems with regression in the real world.

Confounders – variable that is correlated with both the outcome and other variables in the model

Complicated Interactions – how do the covariates interact

Skewness – is the data not evenly distributed, heavy to one side or the other

Outliers – data points that don’t fit the pattern

Non-linear Patterns – not all datasets can be fit with a straight line

Variance Changes

Units/Scale issues – make sure the units are standard across the model

Overloading Regression – too much complexity

Correlation does not imply Causation

What other problems do you find when using Regression on real-world data

Do you know of other problems that are missing.

Like this:

LikeLoading...

Related

4 thoughts on “9 problems with Real World Regression”

Small Sample – absence of sufficient data to fit a regression model.

That is a common problem. Professor Jeff Leak did not add that to his list. I wonder if that problem is not specific to Regression, because all statistical/machine learning models suffer when not enough data is present. I would agree with you though; small sample size can be a problem when doing any data analysis.

Small Sample – absence of sufficient data to fit a regression model.

LikeLike

That is a common problem. Professor Jeff Leak did not add that to his list. I wonder if that problem is not specific to Regression, because all statistical/machine learning models suffer when not enough data is present. I would agree with you though; small sample size can be a problem when doing any data analysis.

Thanks for commenting,

Ryan

LikeLike