Entrance Cut-off Analysis
🎓📈 Make an Informed Decision for Your Future 📊🌟
CUET(PG) scores are out, and we understand the tension you're feeling as you try to figure out where you stand among the crowd. We've been through it too, and we can totally relate! Your next decision could be life-changing, whether it's choosing a non-CUET university as a backup plan or waiting for the official cut-offs to be announced.
But worry not! The Economiga team is here to help you solve this dilemma.
Just submit your part-wise scores and the universities you intend to apply to, and our website will work its magic! We offer you in-depth analytics and live statistics based on the data you provide, so you can make an informed decision before the official results.
🧪How it works
Disclaimer before you try our tool
Please note that these are mere predictions based on partial information that is available to us.
We make use of the limited information to devise an algorithm to get close the actual cut-off, hence there is scope for error.
This should only be used as an indicative measure of where you stand to some extend. To be specific, if you are way below the cut-off you could assume you might not really qualify for that university with a high probability. But, it does not necessarily mean if you are pass the cut-off and you will surely get through.
If you see "#REF!" it means that we haven't reached the minimum sample to generate the cut-off. The best way to make it visible is to share the form with maximum number of CUET aspirants so that it reaches the threshold sample to generate the cut-off. It so happens due to the fact that many of these universities have very low admit rate (eg. 0.005, 0.009 etc) which means we need to have at least 50 to 100 sample data points to get a whole digit rank. This is essential in our prediction as we make use of this whole digit rank to predict the underlying cut-off.
Our tool works with a standard formula and totally depends on the "Law of Large Numbers" which states - as sample size increases the sample mean converges to population mean. Hence, more the data we get, better the insights. Given this, it is so advised that you share it maximum among your network.
All the predictions are for the general cut-off. Hence, it is for you to assume that the reserved cut-offs will be strictly lower than our predictions.
In order to compute the cut-off one requires prior information about the course specific admit rate to a particular university. By admit rate we mean, the number of seats divided by the total number of applicants. Since we are finding specifically the general cut-off, we referred to the respective university prospectus for the number of general seats offered via CUET(PG) entrance test. However, we do not have any information about the number of applicants for each of the courses and universities. In this case, we assume the best possible applicant number based on previous year statistics and general consensus.
Once we have the admit rate, we now multiply our sample size for a particular course with the admit rate for that course to obtain the sample specific cut-off rank.
Once this information is available, we find the cut-off marks corresponding to the sample cut-off rank.
This is one of the open initiatives by team economiga to bridge the asymmetry in the realm of masters economics entrance. In the past years, we have observed that students are extremely stressed due to delayed release of scores from universities which lead them to commit early to other universities that has early admits that creates lots of mental and financial sufferings.