![]() ![]() If the statistics say that no relationship exists, the pattern could have occurred by random chance. If a line is not clear, statistics ( N and Q) determine whether there is reasonable certainty that a relationship exists.When the data are plotted, the more the diagram resembles a straight line, the stronger the relationship.Both may be influenced by a third variable. Even if the scatter diagram shows a relationship, do not assume that one variable caused the other.Keep increasing the separation between the two times until the scatter diagram shows no correlation. If the scatter diagram shows correlation, do another diagram where variable B is the measurement two times previously. Variable B is the same measurement, but at the previous time. ![]() To test for autocorrelation of a measurement being monitored on a control chart, plot this pair of variables: Variable A is the measurement at a given time.Plot number of people trained versus number of calls. You suspect that more training reduces the number of calls. Variable A is the number of employees trained on new software, and variable B is the number of calls to the computer help line.Plot temperature and color on a scatter diagram. You suspect higher temperature makes the product darker. Variable B measures the color of the product. Variable A is the temperature of a reaction after 15 minutes.Scatter Diagram Example Additional Scatter Diagram Examplesīelow are some examples of situations in which might you use a scatter diagram: Therefore, the pattern could have occurred from random chance, and no relationship is demonstrated. Then they look up the limit for N on the trend test table. Q = the smaller of A and B = the smaller of 18 and 6 = 6 ![]() To test for a relationship, they calculate:Ī = points in upper left + points in lower right = 9 + 9 = 18ī = points in upper right + points in lower left = 3 + 3 = 6 Median lines are drawn so that 12 points fall on each side for both percent purity and ppm iron. Purity and iron are plotted against each other as a scatter diagram, as shown in the figure below. The ZZ-400 manufacturing team suspects a relationship between product purity (percent purity) and the amount of iron (measured in parts per million or ppm).
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