How to interpret a linear model

  1. Linear relationship between predictors and outcome.
  2. Independent residuals
  3. Normality of residuals
  4. Homoscedasticity

Linear relationship between predictors and outcome.

Non-linear relationship on the left, log-transformed on the right

Independent residuals

Normality of residuals

Homoscedasticity

  1. Beta coefficient
  2. Significance value (P-value)

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James Parkin

James Parkin

Medical Doctor and Data Scientist living in London. I write about novel Machine Learning techniques being used to solve Healthcare’s biggest problems.