This paper aims to provide a methodology for the quantitative analysis of migraine data. The main objective is to facilitate the health practitioners who are fascinated by data study and have it in mind to offer a concise steer that may prove useful across a wide range of medical applications. To illustrate the proposed study, a typical migraine dataset is used to demonstrate how these steps are useful in practice. However, nowadays migraine becoming a common problem in almost all kinds of people. Due to stress full working environment, the impact of parent’s heredity in children, lifestyle change, irregular food habits, weather conditions, excess consumption of caffeine, medication overuse, menstrual time headache, and menopause stress in women and tension are the reasons for a migraine attack. The data set Kostecki Dillon downloaded from the UCI repository with 4152 observations on 133 subjects for 9 variables is considered for the learning and from this, separation of records on migraine handling collected by Tammy Kostecki-Dillon consists of headache entries set aside in a treatment program. The study will discuss some standard ideas of correlations and p – values to quantify “importance” (or more mathematically accurately statistical significance). Also, it discusses some standard statistical analysis and hypothesis testing which offers an improved understanding.

Keywords : Migraine; Statistical analysis; Hypothesis testing; Correlation)