It is saying that to understand God's thoughts we must study statistics; they are the measure of his purpose. More over the correlation is one of the methods of statistics which used between two continuous variables. The correlation is a very simple method to interpret the possible linear association. As we know that all the methods can be misused at some aspects similarly correlation can also be misused by some researchers. It has many uses in medical research. In this article we will be highlighting uses and calculation hints of correlation.
1) The very first thing to understate in correlation is its definition. Many students fail to understand the definition. “Correlation is a method of assessing a possible two-way linear association between two continuous variables”. It is also called correlation coefficients is statistics.
2) There are two types of correlation coefficients. First is “Pearson's product moment correlation coefficient” and second is “Spearman's rank correlation coefficient”. Some more other types of correlation are used depends on variables being studied.
3) When the values increase together the correlation is positive otherwise correlation is negative when any one value decreases. For zero value correlation doesn’t exist.
4) Correlation can work for curve data also but it can nicely calculate for straight line data.
5) A correlation is not causation. It means that the correlation always supports that there could be any other reasons for good or bad correlation of data.
The above hints are helpful to calculate and to understate that the correlation coefficients are used in medical and pair of variables linear relations.
1) The very first thing to understate in correlation is its definition. Many students fail to understand the definition. “Correlation is a method of assessing a possible two-way linear association between two continuous variables”. It is also called correlation coefficients is statistics.
2) There are two types of correlation coefficients. First is “Pearson's product moment correlation coefficient” and second is “Spearman's rank correlation coefficient”. Some more other types of correlation are used depends on variables being studied.
3) When the values increase together the correlation is positive otherwise correlation is negative when any one value decreases. For zero value correlation doesn’t exist.
4) Correlation can work for curve data also but it can nicely calculate for straight line data.
5) A correlation is not causation. It means that the correlation always supports that there could be any other reasons for good or bad correlation of data.
The above hints are helpful to calculate and to understate that the correlation coefficients are used in medical and pair of variables linear relations.
