A **Scatter Diagram** is also known as** Scatter Plot** or **Scatter Graph** is one of the seven basic tools of quality like pareto chart or control chart. It is a kind of mathematical graph which shows values for commonly two variables for a group of numerical data. Horizontal and vertical axis are used to determine the position of each variable of the data set. The points will form a line shape if the variables are correlated. Some PMP aspirants find it difficult to understand because other charts use lines or bars to demonstrate data sets, while a **scatter diagram** uses only points. However, **scatter diagram** is very easy to understand like the other charts and diagrams.In this article we will review the basics of **Scatter Diagram (Scatter Plot, Scatter Graph)**.

**Scatter Diagram (Scatter Plot, Scatter Graph)**

There are seven basic quality management tools for planning, monitoring and controlling processes to improve the quality related issues within the organization. Scatter Diagram (Scatter Plot , Scatter Graph ) is one of them.

The PMBOK Guide 6th edition defines the the scatter diagram as follows;

“A scatter diagram is a graph that shows the relationship between two variables. Scatter diagrams can demonstrate a relationship between any element of a process, environment, or activity on one axis and a quality defect on the other axis.”

The scatter diagram is created with two variables, usually first variable is under the conrol of the researcher and the second variable depends on the first one. The independent variables which effects the dependent ones are typically plotted along the horizontal axis (X – axis) and the dependent variables are plotted along the vertical axis (Y- axis).

The independent variable effects the dependent variable therefore the independent variable is also known as the control parameter.

Sometimes both variables may be independent. In that case the scatter diagram (Scatter Plot , Scatter Graph ) demonstrates the level of correlation between them. In other words, the scatter diagram helps to determine how closely the variables are related.

**Scatter Diagram (Scatter Plot, Scatter Graph) Example**

For example, to understand the relationship between the number of acidents and long working hours within a project, a HSE manager collects data for the two variables: number of accidents and working hours. Then the HSE manager plots the data in a scatter plot by assigning the “working hours” to the horizontal axis (X- axis) and the “number of accidents” to the vertical axis (Y- axis).

The scatter plot of all the data in the research helps the HSE manager to understand the relationship between the two variables. He notices that as the working hours increases, the number of accidents also increase. “Working hours” is the independent data and the number of accidents depends on the working hours.

Below table shows the working hours and accidents within a project.

| |

Working Hours | Accidents |

11000 | 11 |

12000 | 17 |

13000 | 24 |

14000 | 31 |

15000 | 40 |

16000 | 47 |

17000 | 57 |

18000 | 66 |

19000 | 80 |

Below is the same data as a Scatter Plot.

**Scatter Diagram (Scatter Plot, Scatter Graph) & Correlation**

Correlation is used to define the relationships between the variables. In other words correlation shows how the variables relate to each other. Scatter diagrams can be categorized according to the slope of the data points. Three types of correlation: positive, negative, and none (no correlation) may be shown in the scatter diagrams based on the data set and variables.

**Scatter Diagram (Scatter Plot, Scatter Graph) with No Correlation**

If there is no possible relationship between the variables, the correlation type is be called “no correlation”. Also it can be named as zero correlation type. The two variables are not linked. In that case you cannot draw any line through them. For example air temperature and shoe size have no correlation; as the air temperature increases, shoe size is not effected.

**Scatter Diagram (Scatter Plot, Scatter Graph) with Negative Correlation**

In this type of correlation one variable increases as the other variable decreases. For example if the speed increases, travel time to a destination decrease.

** Positive Correlation**

If there is a positive correlation between the variables, this means that if one variable increases the other one increases and if one of them decreases the other one decreases. For example as the speed of a turbine increases, the amount of electricity that is generated increases.

** **

**Strong and Weak Correlation**

If the variables are a bit closer to each other, this means that there is a weak correlation between them. It can also be called as “Scatter Diagram with Low Degree of Correlation”. If the variables are closer to each other, this means that there is a strong (high degree of) correlation between the variables.

**Conclusion**

Scatter diagrams are used to understand the relationships between variables. They are very easy to create and use. If the data don’t cover a wide enough range, the relationship between the variables are not apparent. In some cases both of the two variables may be affected by a third variable. Some PMP aspirants confuse the fishbone (Ishikawa )diagram with the scatter plot. The fishbone (Ishikawa )diagram enables to define the root cause of a problem where the scatter plot helps to look for a relationship between variables.

**External References**

[1]: ASQ: The Global Voice of Quality

**See Also**

Fishbone Diagram (Cause and Effect or Ishikawa)