Statistical analysis is the process of collecting and analyzing data to find patterns in the data. This can be done by looking at a single variable or multiple variables together.
Correlation is a measure of how two variables change together, such as height and weight. The correlation coefficient can range from -1 (a perfect negative relationship) to +1 (a perfect positive relationship).
Correlation coefficients are a measure of the degree of linear relationship between two variables.
There are three types of correlation coefficients, which are Pearson's product-moment coefficient, Spearman's rank-order coefficient, and Kendall's tau.
Assumptions that Impact the Meaningfulness of a Correlation Coefficient
A correlation coefficient is a statistic that measures the strength of a linear relationship between two variables. The correlation coefficient is always between -1 and 1, where 1 means that two variables are perfectly correlated, 0 means no linear relationship between the two variables, and -1 means a perfect negative linear relationship.
The interpretation of the correlation coefficient depends on the assumptions made about it. This article will discuss some of these assumptions and how they can impact the meaning of this statistic.