Model 1
... parameter estimates. There is another serious consequence of adding too many variables to a model. If a model has several variables, it is likely that some of the variables will be strongly correlated. This property, known as multicollinearity, can drastically alter the results from one model to ano ...
... parameter estimates. There is another serious consequence of adding too many variables to a model. If a model has several variables, it is likely that some of the variables will be strongly correlated. This property, known as multicollinearity, can drastically alter the results from one model to ano ...
Class 24 Lecture
... • Logic: Both fixed & random effects models are consistent if models are properly specified • However, some model violations cause random effects models to be inconsistent – Ex: if X variables are correlated to random error ...
... • Logic: Both fixed & random effects models are consistent if models are properly specified • However, some model violations cause random effects models to be inconsistent – Ex: if X variables are correlated to random error ...
