Pearson R Test. Whenever any statistical test is conducted between the two variables then it is always a good idea for the person doing analysis to calculate the value of the correlation coefficient for knowing that how strong the relationship between the two variables is. The pearson correlation coefficient measures the linear relationship between two datasets.
So for example you could use this test to find out whether people s height and weight are correlated they will be the taller people are the heavier they re likely to be. Details the pearson test statistic is p c i e i 2 e i where c i is the number of counted and e i is the number of expected observations under the hypothesis in class i. The correlation coefficient should not be calculated if the relationship is not linear.
The code to run the pearson correlation in r is displayed below.
Lecture video just like with other tests such as the z test or anova we can conduct hypothesis testing using pearson s r. The pearson correlation coefficient is used to measure the strength of a linear association between two variables where the value r 1 means a perfect positive correlation and the value r 1 means a perfect negataive correlation. The calculation of the p value relies on the assumption that each dataset is normally distributed. Pearson s correlation coefficient r is a measure of the strength of the association between the two variables.
