In today’s class professor discussed about the P-Value. Firstly, professor have shown us a video which explains an example on the p – value. P value is used to determine the strength of evidence against the Null Hypothesis. For example, a scientist in lab, experiments with a new drug by giving it to the few people and compare their health conditions with other group of people who has not taken the medicine, after the experimentation process, he gets a p- value ( a numerical value) which helps him to determine whether the results occurred are likely or unlikely to be happened by a random chance. If the p value is small typically < 0.05 then , it shows that results occurred are unlikely to be happened by a random chance. If the p value is large typically greater > 0.05 then , it shows that the results occurred are very likely to be happened by a random chance.
Project Update:
Today I have tried to integrate three datasets ( Inactivity, Obesity and Diabetes ). After combining the three datasets, I saw that there are so many missing values by which we cannot proceed with the further procedure. So After checking the file common-fips for all three datasets, This file was a lead/clue given by the professor for dealing with the entire data. I have used that word document to extract the common FIPS from all three datasets and create a new dataset which does not contain any missing values. The below image shows the procedure of how I combined all three datasets.
