Not sure if it would make a practical difference. Its just a small percentage loss for the deviance.Dale said:That depends on the application. Does it make a practical difference in the intended purpose?
A larger model (more terms) will always fit the data better. In extreme cases this is just fitting to noise. You need to penalize the less parsimonious model. Two common ways of doing so are the AIC or the BIC.
Deviance is a term used in statistics to describe the difference between an observed value and the expected value. It is a measure of how much a data point or group of data points differs from the average or predicted value.
Deviance is typically calculated using a statistical model, such as a regression model, where the predicted value is compared to the observed value. The difference between the two is squared and added together to get the deviance value. This process is repeated for all data points and the total deviance is calculated.
A large change in deviance refers to a significant difference between the observed and expected values. This means that the data points are deviating significantly from the predicted trend or average, and indicates that there may be some important factors or variables influencing the data.
A large change in deviance can be an indication of something interesting or unexpected happening in the data. It could also suggest that there are variables or factors that are not accounted for in the statistical model, and should be further investigated. In some cases, a large change in deviance may also indicate that the model needs to be adjusted to better fit the data.
Yes, a large change in deviance can be either positive or negative. A positive change indicates that the observed values are higher than expected, while a negative change indicates that the observed values are lower than expected. The direction of the change is important in interpreting the impact of the deviance on the data.