What do large coefficients mean?
In the regularisation context a “large” coefficient means that the estimate’s magnitude is larger than it would have been, if a fixed model specification had been used. It’s the impact of obtaining not just the estimates, but also the model specification, from the data.
How do you interpret a positive interaction coefficient?
A positive value for the effect of the interaction term would imply that the higher the income, the greater (more positive) the effect of intentions on behavior was. Similarly, the higher the intentions, the greater (more positive) the effect of income on behavior.
Can a small logistic regression coefficient have a large effect?
This can occur if the predictor variable has a very large range. In the case of this model, it is true that the monthly charges have a large range, as they vary from $18.80 to $8,684.40, so even a very small coefficient (e.g., 0.004) can multiply out to have a large effect (i.e., 0.004 * 8684.40 =34.7).
Is the interaction to be conceptualized in logistic regression?
If the differences are not different then there is no interaction. But in logistic regression interaction is a more complex concept. Researchers need to decide on how to conceptualize the interaction. Is the interaction to be conceptualized in terms of log odds (logits) or odds ratios or probability?
What do you call an exponentiated logistic coefficient?
Many people call all exponentiated logistic coefficients odds ratios. But as you can see from the table above, exponentiating the interaction is a ratio of ratios and the exponentiated constant is the baseline odds. We can compute the odds ratios manually for each of the two levels of f from the values in the table above.
What is the standard error of logistic regression?
The standard error is a measure of uncertainty of the logistic regression coefficient. It is useful for calculating the p-value and the confidence interval for the corresponding coefficient. From the table above, we have: SE = 0.17.