1. LOGISTIC REGRESSION 1

2. PROBABILITIES AND ODDS2

3. 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

4. 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)

5. 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

6. 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;∞>

7. LOGIT 7

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