I am getting confused between Log Likelihood and LLnull.
1) Log likelihood describes how likely is the model in hand describes the underlying relationship of the variables (is this the relationship between independent variables)-If not then why i need log-likelihood when we can find the relationship of the independent variable with predicted variable through p value
2) LL null explains the log likelihood of a model which has no independent variable- If it is less than loglikelihood then does it mean that our model has independent variables which is not zero (Please clarify)
LL null = Log Likelihood when there are no independent variables (correct).
Log likelihood is a measure of goodness of fit (how well your data is fitting your model). It does not say how likely it is, but is just a number which shows how well we describe the relationship.
When you are comparing the Log-likelihood of your model with LL-null, you are comparing your model with the nothingness model (model with no predictors).
On average we always expect a better value from Log-likelihood than from LL-null.
Their ratio is a ‘statistical measure’ which shows if they are significantly different. If they are => your model is useful!
If they are statistically the same => your model is as good as the nothingness model, so it is useless.
Hope this helped!
The 365 Team