LOGISTIC REGRESSION 1

PROBABILITIES AND ODDS 2

Probabilities and odds Odd (O*) is the ratio of probability of the occurence of the event and probability of the non-occurence of the event Practical example 2x2 table Interpretation (middle value =1, asymmetric scale, reversing of ration if O<1) * O will be used for odd

Logistic regression Dep. var: binary or nominal/ordinal Binary – only two values, impossible to directly model by linear regression (picture) Necessary to transform original dep. var. (see probability-odd-logit)

Odds ratio Ratio of two odds – OR* * OR will be used for odd ratio Practical example 2x2 table Interpretation (middle value =1, asymmetric scale, reversing of ratio if O<1) * OR will be used for odd ratio

Why we should use odds and odds ratios? Descriptive stats for contingency tables Decrease nr. of estimated parameters (see loglinear models) Change scale for probablity <0;1> into larger one <0;∞>

LOGIT 7

probability≠ odd ≠logit Logarithm of odd or odds ratio Theoretical values? Conclusion: probability≠ odd ≠logit