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Difference between linear regression and logistic regression

Linear and logistic regression

  • In linear regression, the outcome (dependent variable) is continuous. It can have any one of an infinite number of possible values.
  • In logistic regression, the outcome (dependent variable) has only a limited number of possible values.
  • Logistic regression is used when the response variable is categorical in nature. For instance, yes/no, true/false, red/green/blue, 1st/2nd/3rd/4th, etc.
  • Linear regression is used when your response variable is continuous. For instance, weight, height, number of hours, etc.
  • Linear regression gives an equation which is of the form Y = mX + C, means equation with degree 1. However, logistic regression gives an equation which is of the form Y = eX + e-X
  • In linear regression, the coefficient interpretation of independent variables are quite
    straightforward (i.e. holding all other variables constant, with a unit increase in this variable, the
  • The dependent variable is expected to increase/decrease by xxx).
  • In logistic regression, depends on the family (binomial, Poisson, etc.) and link (log, logit, inverse-log, etc.) you use, the interpretation is different.