1.
with a normal distribution. Often, though, we have data where the interest is in predicting or “explaining” a binary (or dichotomous) outcome variable by a set of explanatory variables. This type of outcome variable is a two-category variable. Examples are:
Success/failure of a treatment, explained by dosage of medicine administered, patient’s age, sex, weight and severity of condition.
High/low cholesterol level, explained by sex, age, whether a person smokes or not, etc.
Use/non-use of contraception, explained by gender, age, whether married, education level, religion, etc.
Vote for/against political party, explained by age, gender, education level, region, ethnicity, etc.
Yes/No or Agree/Disagree responses to questionnaire items in a survey.
Logistic regression is the most popular technique available for modelling dichotomous dependent variables.
2.
Lets get technical just for a while! Going back to the simple linear regression model for a our outcome variable Y. Statisticians often use the notation E[Y] for the mean of the variable Y in the population – the E stands for “expected value”. Our model assumed that this mean was related to the value of the explanatory variable, x, via the linear equation
E[Y]=β0+β1x.
(2.1)
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