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Run Chart is a plot of the data points for a particular metric with respect to time. It is primarily used for following two purposes 1. Graphical representation of performance of the metric (without checking for any patterns in it). E.g. The scoring comparison in a cricket match. The runs are plotted on Y axis and X axis has overs (which is a substitute for time spent) Source: The Telegraph 2. To check if the data from the process is random or if there is a particular pattern in it. These patterns could be one or more of the following a. Clusters b. Mixtures c. Trends d. Oscillations Source: Minitab help section Run chart if used for point number 2, performs following tests for randomness - Test for number of runs about the median. This is used for checking Clusters and Mixtures. Clusters are present if the actual number of runs about the median is less than the expected runs. This implies that there are data points in one part of the chart Mixtures are present if the actual number of runs are more than expected runs. This implies that there are frequent crossings of the median line - Test for number of runs up or down. This is used for checking Trends and Oscillations. Trends are present if the actual number of runs is less than the expected runs. This implies that there is a sustainable drift in the process (either up or down) Oscillations are present if the actual number of runs is more than the expected runs. This implies that the process is not steady These are hypothetical cases with the below hypothesis Ho - Data is random Ha - Data is not random p values are calculated for all the 4 patterns. A p value of less than 0.05 indicates acceptance of Ha implying that the particular pattern is present in the data set. Absence of these patterns indicate that the process is random. Advantages of Run chart over Control chart Ideally control chart is a more advanced tool as compared to a run chart. However following situations warrant the use of run chart over a control chart 1. Run chart is preferred when we need a snapshot of the metric performance with time without taking into account the control limits or if the process is stable/unstable. E.g. like the scoring run rate comparison for cricket (refer the screenshot above) 2. One can start creating run chart without any prior data collection unlike in a control chart (where data is collected first to determine the control limits) 3. As a quick check to see if the process data is random or not. For doing such checks (clusters, mixtures, trends and oscillations) in a control chart, one would have to run all the Nelson tests (usually control charts are used with only one test i.e. any points outside 3 standard deviations and hence might not be able to detect such patterned data) 4. Apart from the above, it is easy to prepare and interpret a run chart in comparison to a control chart