This list comes from the Coursera Data Analysis Course.

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.

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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.

Thanks for commenting,

Ryan