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No, it doesn't mean that. The model with the intercept is usually uninteresting and whether the intercept is significant isn't important. I don't think there's a sensible way of making the intercept significant, but there's no reason to do so. So, you can add independent variables.


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With respect to "None of the variables in the equation are significant should I still interpret B and exp(B) and the results?", I would interpret the result to imply, that to date in the analysis with respect to the proposed explanatory variables presented, a crab's mating decision largely remains still a random decision with a sample estimated probability. ...


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This study, unfortunately, is probably too small. With respect to your questions: For logistic regression, an often useful rule of thumb is that you should have about 15 members of the smallest outcome category per predictor that you are evaluating in a model. Otherwise you are likely to be overfitting; see this page for example. With 24 cases total you can'...


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Item variances are rarely constant across items in factor analysis. Most typically, items are only congeneric, which means that their loadings on the common factor and their residual variances both may differ across items, so that their total variances may differ. So there is nothing unusual here. Your observed variables are averages--does that mean equally ...


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