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Lecture

L18 - Logistic Regression
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Department
Political Science
Course
POL3371
Professor
Ivan Katchanovski
Semester
Winter

Description
March 25, 2014 Logistic Regression Binary Logistic Regression  Appropriate when the dependent variable is a dummy variable o Dummy variable: a variable the includes two categories which assume values 1 and 0 o Example: “Voted”: Yes = 1, No = 0  One or many independent variables  Assumes non-linear relationship Regression Coefficients  Interpretation is similar to interpretation of unstandardized regression coefficients in linear regression o Effect of ca change of one unit of an independent variable on the logged odds of the dependent variable. Odds Ratio … Statistical Significance  Statistical significance of a regression coefficient: o Statistically significant if p(obtained) < .05 or .01 or .001 o Statistically non-significant if p(obtained) > .05 o For smaller samples .1 level of statistical significance can also be used. Pseudo R Square  R square analogs in logistic regression o Power of independent variables in predicting the dependent variable  Cox & Snell R square o Ranges between 0 (no association) and less than 1 (perfect association)  Nagelkerke R square o Adjusts Cox & Snell R square so that its maximum value can equal 1 o Ranges between 0 (no association) and 1 (perfect association) Binary Logistic Regres
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