Totally curious…anyone in the same boat as me having a hard time remembering all the decision rules when to accept and reject the null hypothesis for all the various tests in L2 Quants (particularly the regression parts)? Would love to find a resource that summarizes and explains everything nicely and briefly.
Also a little bit confused but am I missing something here? Some people are able to look at the values of the calculated t-statistics shown in tables from the various examples in the CFA curriculum and easily tell that the variable is significant/not significant without them referring to a critical value. I always thought t-stats should always be compared against a critical value (which you can then obtain based on the specified significance level and df).
Is there like a general rule that will allow me to easily tell if the t-stat value will cause the variable to be significant, just by looking at it and without actually comparing it against a critical value?
Not sure if my question totally makes sense but hoping for some answers. Thank you!!