Logistic regression

Housing statistics and English Housing Survey, glossary, published by the Department for Levelling Up, Housing and Communities in 2019, defines logistic regression as: ‘…a regression model where the dependent variable is binary i.e. takes one of two values which are assigned as 0 or 1. The model predicts the probability of the dependent variable taking the value 1 for particular values of the independent variables. The regression coefficients are usually estimated using maximum likelihood. Logistic regression measures the relationship between the categorical dependent variable and one or more independent variables by estimating probabilities using a logistic function, which is the cumulative logistic distribution. Thus, it treats the same set of problems as probit regression using similar techniques, with the latter using a cumulative normal distribution curve instead. Equivalently, in the latent variable interpretations of these 2 methods, the first assumes a standard logistic distribution of errors and the second a standard normal distribution of errors.’

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