Progressive terms assumption is a statistical assumption in which the probability of an event or outcome increases as time goes on. It is often used in analyzing longitudinal data or data collected over a period of time.
The main difference between these two assumptions is the concept of time. While progressive terms assumption assumes that the probability of an event changes over time, stationary terms assumption assumes that the probability remains constant over time.
Progressive terms assumption is important because it allows for the analysis of data that changes over time. Without this assumption, the results of statistical analysis may not accurately reflect the underlying trends in the data.
There are several methods for testing progressive terms assumption, including graphical methods such as plotting the data over time and looking for patterns, and statistical tests such as the Durbin-Watson test and the Breusch-Godfrey test.
Yes, progressive terms assumption can be violated. This can occur when the probability of an event does not actually increase over time, or when there are other underlying trends or patterns in the data that are not accounted for. It is important to carefully assess the data and choose appropriate methods for analysis to ensure that this assumption is not violated.