Scatter plots provide an important tool for visualization. Correlation helps the management come up with the possible levels of these variables so that accurate budgets can be developed. Management has to forecast the levels of many variables before they agree on a budget. Hence experimenters can vary one variable and record the other variable to determine the extent of the correlation. This variable will be known as the independent variable.

Best Used When One Variable is Under ControlĬorrelation must be used only when at least one of the variables is under control. Implying causation could lead to inadvertent losses. Just because we have the statistics to show that the two variables tend to move in tandem does not mean that we have proof that one causes the other. This may or may not be the case in reality. Relationship May or May Not be Cause and EffectĬorrelation is often confused with causation. Suppose the two variables were being recorded and measures and a high degree of correlation existed, it would provide useful information to the management to run the business better. However, perfect correlations do not exist and if you come across one, it should be doubted. +1 shows perfect correlation, while -1 shows perfect negative correlation. Correlation is recorder on a scale of +1 to -1. A good example would be that whenever the cycle time is high, customer dissatisfaction is also high. What is Co-Relation ?Ĭorrelation is the degree to which two variables simultaneously vary. When there are multiple variables involved, it is called a multivariate scatter plot. When it studies the correlation between two variables, it is called a bivariate scatter plot.

The scatter plot studies the correlation between the important variables. It has been designed to ensure that it provides a convenient view of the process to the manager at a single glance.

Scatter plot examples