Asked by Mark
Can The anti-logs of logistic regression coefficients, i.e. Exp(B), can be interpreted as odds multipliers?
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GPT 3.5
Yes, the anti-log of the logistic regression coefficients can be interpreted as odds multipliers. In logistic regression, the coefficients (B) represent the change in the log odds of the outcome variable for a one-unit change in the corresponding predictor variable. Taking the exponent of these coefficients (Exp(B)) gives the odds ratio associated with that predictor.
For example, if the coefficient for a predictor is 0.8, the odds ratio (Exp(0.8)) would be approximately 2.225. This means that for a one-unit increase in the predictor variable, the odds of the outcome variable would increase by a factor of 2.225. Similarly, for a one-unit decrease in the predictor variable, the odds would decrease by the same factor.
So, the anti-log of the logistic regression coefficients provides the odds multipliers, which help interpret the effects of predictors on the outcome variable in logistic regression.
For example, if the coefficient for a predictor is 0.8, the odds ratio (Exp(0.8)) would be approximately 2.225. This means that for a one-unit increase in the predictor variable, the odds of the outcome variable would increase by a factor of 2.225. Similarly, for a one-unit decrease in the predictor variable, the odds would decrease by the same factor.
So, the anti-log of the logistic regression coefficients provides the odds multipliers, which help interpret the effects of predictors on the outcome variable in logistic regression.
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