Binary Logistic Regression with Categorical predictor in STATA

Dependent, sample, P-value, hypothesis testing, alternative hypothesis, null hypothesis, statistics, categorical variable, continuous variable, assumptions, standard deviation, standard error, mean, dependent variable, independent variable, grouping variable, descriptive statistics, significance, one sided tail, two sided tails, 95% confidence interval, upper, lower, average, exposure, explanatory, statistics, statistical method, continuous variable, Descriptive, output, table, descriptive statistics, statistically significance value, Binary, dichotomous, Odds ratio, OR, frequency, likelihood ratio, observation, standard error, z-score, outcome, response, category, parameter coding, percent, cases, missing cases, predictor, Wald test, degree of freedom(df), Exp(B), chi-square, constant, B value, Cox & Snell R Square, -2 Log likelihood, Nagelkerke R Square, Hosmer and Lemeshow Test, contingency table, observed, expected, correlation matrix, probability.

[@ebenezerefichie28]

Thank you so much. This has really been helpful.

[@farahmejri5157]

When computing the probability from the odds ratio for cigarettes, for example, should we add the constant odd ratio to the cigarette odd ratio? Or how does it usually work?

[@splham]

what is the logic of subtracting the odds ratio from 1? please

[@m.walidhemat6319]

odds ratio of 1 indicates zero relation and it is the baseline

[@ekokofredrick2062]

How did you ges 178% ?

[@StatisticsTutor]

Subtract from 1 then multiply by 100