A correlation coefficient of 1 or -1 would be the highest possible statistical relationship. However, the calculation of correlation coefficients between non independent values or small sets of data may show high coefficients when no relationship exists.
The product-moment correlation coefficient or PMCC should have a value between -1 and 1. A positive value shows a positive linear correlation, and a negative value shows a negative linear correlation. At zero, there is no linear correlation, and the correlation becomes stronger as the value moves further from 0.
A graph of Charles Law shows the relationship between temperature and volume of gas.
Any graph where, from left to right, the slope goes upward (assuming the axes are labelled in the standard way).
it is called an inequality
Mapping Diagram
If variables have zero correlation, they do not have a linear relationship. Zero correlation shows that two things were not found to be related.
No correlation.
Trend correlation
If two variables are highly correlated, the Pearson correlation will be close to -1.0 or +1.0. A correlation of zero shows no relationship.
D. Trend correlation
A scatterplot with no correlation means that there is no relation between the two categories, a negative correlation means that the two categories have a relationship that as one gets greater the other gets smaller
correlation
Multicolinearity shows the relationship of two or more variables in a multi-regression model. Auto-correlation shows the corellation between values of a process at different point in times.
correlation
The product-moment correlation coefficient or PMCC should have a value between -1 and 1. A positive value shows a positive linear correlation, and a negative value shows a negative linear correlation. At zero, there is no linear correlation, and the correlation becomes stronger as the value moves further from 0.
A correlation coefficient is a value between -1 and 1 that shows how close of a good fit the regression line is. For example a regular line has a correlation coefficient of 1. A regression is a best fit and therefore has a correlation coefficient close to one. the closer to one the more accurate the line is to a non regression line.
Correlation