# When to normalize data in regression? [duplicate]

Under what circumstances should the data be normalized/standardized when building a regression model. When i asked this question to a stats major, he gave me an ambiguous answer "depends on the data".

But what does that really mean? It should either be an universal rule or a check list of sorts where if certain conditions are met then the data either should/ shouldn't be normalized.

• It should either be an universal rule or a check list of sorts where if certain conditions are met then the data either should/ shouldn't be normalized. Can you justify that? – Matthew Drury Mar 15 '16 at 21:45
• Are you asking about standard linear regression, or about penalized methods like ridge regression or LASSO? – EdM Mar 15 '16 at 21:46
• @MatthewDrury: What i mean is either data should be normalized for building all regression models (OLS, Logistic etc) or it should be done when so and so conditions are not satisfied like non-constant variance..etc (hypothetically speaking) – Raj Mar 15 '16 at 21:50
• This page has an extensive discussion of situations in which normalization/standardization might be important. Please look it over and edit your question to include any issues that you believe weren't discussed adequately there. – EdM Mar 15 '16 at 21:59
• Much software will automatically standardize the variables for its internal calculations. Could you clarify, then, whether you are asking about how to compute numerical solutions or about whether it is actually necessary to standardize the variables before applying regression software? – whuber Mar 15 '16 at 22:47