Tools And Techniques

Abstract

Decision making requires the use of various tools and techniques. In order for an individual to justify a decision, they must be able to identify and implement a tool or technique that can provide the appropriate data. The tool that I will discuss in this paper is called a scatter diagram. Scatter diagrams or scatter plots as some would refer to them as, show us the relationship between two variables that relate to the same event. The diagram consists of a vertical axis and a horizontal axis. The scatter diagram was developed so that intuitive and qualitative conclusions could be drawn about the paired data, or variables.

Scatter Diagram

A scatter diagram is a tool that is used for analyzing the relationship between two variables. One variable is plotted on a horizontal axis and the other is plotted on a vertical axis. The pattern of their intersecting points can graphically show relationship patterns (C.U., 2005).

The origin of the scatter diagram came from Sir Francis Galton's study of linear regression. The scatter diagram was developed so that intuitive and qualitative conclusions could be drawn about the paired data, or variables. The concept of correlation was employed to decide whether a significant relationship existed between the paired data. Furthermore, regression analysis was used to identify the exact nature of the relationship (Medizinfo, 2006).

Scatter diagrams can be used when there seems to be a relationship between two variables. It can also be used when the data is continuous, such as temperature, time and numbers. This method of determining the relationship is a fast and easy way to test the correlation between two variables (QA Project, 2006).

The basic steps involved in constructing a scatter diagram are as follows:

1. Define the x variable on a graph paper scatter diagram form. This variable is often thought of as the cause variable and is typically plotted on the horizontal axis.

2. Define the y variable on the diagram. This variable is often thought of as the effect variable and is typically plotted on the vertical axis.

3. Number the pairs of x and y variable measurements consecutively. Record each pair of measures for x and y in the appropriate columns. Make sure the x measures and corresponding y measures remain paired so the data are accurate.

4. Plot the x and y data pairs on the diagram. Locate the x value on the horizontal axis; then locate the y value on the vertical axis. Place a point on the graph where these two intersect.

5. Study the shape formed by the series of data points plotted. In general, conclusions can be made about the association between the two variables (x and y) based on the shape of the scatter diagram. Scatter diagrams that display associations between two variables tend to look like elliptical spheres or even straight lines.

6. Scatter diagrams on which the plotted points appear in a circular fashion show little or no correlation between x and y.

7. Scatter diagrams on which the points form a pattern of increasing values for both variables show a positive correlation; as values of x increase, so do values of y. The more tightly the points are clustered in a linear fashion, the stronger the positive correlation, or the association between the two variables.

8. Scatter diagrams on which one variable increases in value while the second variable decreases in value show a negative correlation between x and y. Again, the more tightly the points are clustered in a linear fashion, the stronger the association between the two variables (ASQ, 2002)

Listed below are samples of scatter diagrams. In figure 1, the strong correlation indicates that there is a close relationship