LEVEL II BIC = nxln(SSE/n) + ln(n)x(k+1) n: number of observations, k: number of parameters BIC is a measure of how well the model fits the data, taking into account the complexity of the model and the size of the sample. A lower BIC value indicates a better fitting model, as it suggests that the model is able to accurately …

LEVEL II AIC = nxln(SSE/n) + 2(k+1) n: number of observations, k: number of parameters AIC is a measure of how well the model is able to make accurate prediction, taking into account the complexity of the model. A lower AIC value indicates a better predictor, as it suggests that the model is able to accurately predict the dependent variable …

LEVEL II Adjusted R-squared is a modified version of the R-squared measure of how well the regression model fits the data in a simple linear regression study. It takes into account the number of independent variables in the model and adjusts the R-squared value accordingly. R-squared represents the percentage of the variance in the dependent variable that is explained by …