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Answers
Step-by-step explanation:
The Chi-square goodness of fit test is a statistical hypothesis test used to determine whether a variable is likely to come from a specified distribution or not. It is often used to evaluate whether sample data is representative of the full population.
Answer:
There are at least two reasons for predicted R-square is equal to 0 (it may even be negative). The fist one is when you have unsignificant effects in your model because if you maintain an unsignificant effect in your model, it means that this effect brings noice, and noice is not good for prediction! So eliminate unsignificant effects and prediction R-square will increase (of course classical R will decrease, but I prefer a model wirth R²=70% and pred R²=65% than one with R²=95% and pred R²=5%).
Of course, if LOF is significant, prediction R² will not be good. Then you have two possibilities : increase the degree of your model (if it is possible) or transform your response (Box-Cox transformation).