If all your data points are in a straight line then there is a "perfect" correlation.
This is often used to confirm that there is some association between the two variables.
Plotting a scatter chart will allow you to add a "line of best fit" to your data points.
This line of best fit can be used to estimate a y-value value for a given x-value.
Also called a scatter diagram
A scatter chart is a way of representing a set of paired (bivariate) data.
One variable is plotted on the x-axis and the other on the y-axis
Normally the variable on the x-axis is the independent variable which is the one that influences the variable on the y-axis (the dependent variable)
So y depends on x
If two measures have no association then we have zero correlation which is represented by a correlation coefficient of zero.
If two measures are in perfect association then we have a perfect correlation which is represented by a correlation coefficient of 1.
A relationship between two variables is called monotonic is as one variable increases the other variable also increases
Or alternatively as one variable decreases the other variable decreases
This type of relationship is less restrictive than a linear relationship
A scatter chart may give an indication to a correlation but it will not tell you the extend of the correlation.
Positive Correlation - If the x-value increases then so does the y-value.
Negative Correlation - If the x-value increases then the y-value decreases.
Perfect Position Correlation - Both the x-value and the y-value increase with the same proportion.
Perfect Negative Correlation - Both the x-value and the y-value change with the same proportion.