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.

  1. Confounders – variable that is correlated with both the outcome and other variables in the model
  2. Complicated Interactions – how do the covariates interact
  3. Skewness – is the data not evenly distributed, heavy to one side or the other
  4. Outliers – data points that don’t fit the pattern
  5. Non-linear Patterns – not all datasets can be fit with a straight line
  6. Variance Changes
  7. Units/Scale issues – make sure the units are standard across the model
  8. Overloading Regression – too much complexity
  9. 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.