Main assumptions

• Validity
• Additivity
• Linearity
• Independence and equal variance of errors (we’re talking about OLS)
• Normality of errors (might not be present, but its distribution has to be understood)

Checklist before modeling

• Distribution of the target variable
• Distributions and correlations between exogenous variables
• Missing values and outliers
• Interpretation of values (also applies to outlier identification) and data leakage
• Would an intercept make sense from interpretability point of view?
• Should we expect any variable interactions?

Checklist for transformations

• Where do we need normalization?
• Where do we need one-hot encoding?
  • Do we really need it, can we split some variable values into ordinal bins?
• Any non-linear transformations would make sense here?
• Check correlation and distribution of interactions

Checklist for fit and validation

• Does the sign of the coefficient make sense? Is it statistically significant?
• Does Rsq make sense? Would you expect it to be big or small and why?
• Is Rsq of validation set lower that of a training set?
• distribution and qqplot of residuals, does it make sense? If not, is there a transformation to make things better
  • Shapiro-Wilk, cook distance?
  • VIF is less then 10 for each variable
  • partial regression plots, do they make sense? Should some variables be kicked out?

Things for production

• Do we get enough data, is it clean enough?
• How do we see if that data or the process changed enough to make the model invalid?
• Do we get the data quickly enough for the answer to make sense?
• Is the model interpretable enough and accepted by the business?
• How would the model errors affect the business process? And expected feedback loops?