Ashish K

Sure Vishwadeep, An exam is conducted where the candidates can either pass or fail - this is the output - binary in nature These candidates come from various backgrounds - viz. Profession (8 levels), Age, Area (2 levels), Gender (2 levels) Hypothesis related to all these x's have come with p values lower than 0.05 suggesting association / dependency. Now, I have to find which candidate is likely to pass the exam with highest probability. Hope this helps.

Hi Vishwadeep, Thanks for replying. I think I have not been able to explain it rightly in my post. I have a process whose output is binary - Pass or Fail. This process has an input which has various categories ( 8 in nos.), these categories when processed can either result into either pass or fail output. I wanted to know if there is association between the input category and the process output, so i conducted chi square test on the sample data. The p value of the test has come as 0.00 indicating there Alternate Hypothesis is true and there is an association between input category and the process output. Now, i wish to establish statistically that which input category is beneficial for increasing the pass% in the process i.e. if the existing 8 categories of inputs in the process are controlled in right proportions or only some of the 8 categories are allowed as input then the process can achieve higher efficiency. For this, i need to know by how many units should I increase / decrease a category and by how much units the process would increase or decrease by bringing this change. Hope it clarifies.

I have a process which has various categories of input and success rate for each category. I have collected a sample and executed a chi square test for determining if there is an association between categories of inputs and success rate for my process. My p value is coming as 0.00 indicating that there is an association. Now, I want to know how much is the extent of this association i.e. which category is expected to give more success. How do i use the sample data to determine which category of input should i increase more and more to get higher success rate in my process.