• Ms. I. Angel Agnes Mary

Logistic regression, being useful to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables. In our study, we find that predicting the outcome of arthritis problem over a long run childhood activities. Negative binomial model, being suitable in situations where the response variable is the ‘waiting time’ (in discrete units) for an event to happen. In our study, we find that only those who have arthritis problem and wish to build a model that would help in predicting the likely time for onset of arthritis for the risky people, given various explanatory variables. The objectives of the study are as follows: • To build a model for predicting the outcome of arthritis problem over a long run. • To find the expected time for the patients who have the risk of arthritis.

Arthritis, Logistic Regression, Negative binomial Regression

"Logistic and negative binomial regression models on Factors causing Arthritis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 4, page no.614-617, April-2021, Available :http://www.jetir.org/papers/JETIR2104281.pdf

"Logistic and negative binomial regression models on Factors causing Arthritis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 4, page no. pp614-617, April-2021, Available at : http://www.jetir.org/papers/JETIR2104281.pdf