Simple logistic regression computes the probability of some outcome given a single predictor variable as
age has a considerable positive skewness, especially for the clients who died.īut how can we predict whether a client died, given his age? We'll do just that by fitting a logistic curve.the standard deviation of age is much larger for clients who died than for clients who survived.all but one client over 83 years of age died within the next 5 years.
The raw data are in this Googlesheet, partly shown below.Ĭan we predict death before 2020 from age in 2015?Īnd -if so- precisely how? And to what extent? A good first step is inspecting a scatterplot like the one shown below.Ī few things we see in this scatterplot are that A related technique is multinomial logistic regression which predicts outcome variables with 3+ categories.Ī nursing home has data on N = 284 clients’ sex, age on 1 January 2015 and whether the client passed away before 1 January 2020. This analysis is also known as binary logistic regression or simply “logistic regression”. Logistic regression is a technique for predicting aĭichotomous outcome variable from 1+ predictors.Įxample: how likely are people to die before 2020, given their age in 2015? Note that “die” is a dichotomous variable because it has only 2 possible outcomes (yes or no). Logistic Regression By Ruben Geert van den Berg under Regression & Statistics A-Z
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