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Displays observed data in a time sequence. It is used to identify trends or patterns over time and is measured by plotting individual data points in the order in which they occur.

Measures the ability of an entire process to produce output that meets specifications. It is calculated using the formula $Pp = \frac{USL - LSL}{6\sigma}$, where $\sigma$ represents the overall process variation.

Displays the distribution of a set of continuous data, providing a visual interpretation of numerical data by indicating the number of data points that lie within a range of values. It is measured by dividing the data into classes or bins and counting the number of observations in each bin.

Prioritizes the most significant factors in a set of data. It is measured by calculating the frequency of defects or issues and representing them in descending order on a bar chart, often accompanied by a cumulative line graph.

Examines the relationship between two variables. It is measured by plotting pairs of numerical data, with one variable on each axis, to look for a relationship or correlation between them.

Assesses process performance considering both the process variation and the process mean's shift from the target specification. It is calculated similarly to Cpk but using overall standard deviation, represented as $Ppk = min(\frac{USL - \mu}{3\sigma}, \frac{\mu - LSL}{3\sigma})$.

Evaluates the capability of a process to produce output within specified limits. It is measured as the minimum of two calculations, one for each side of the mean, represented as $Cpk = min(\frac{USL - \mu}{3\sigma}, \frac{\mu - LSL}{3\sigma})$ where $\mu$ is the process mean, $\sigma$ is the process standard deviation, and USL/LSL are the upper/lower spec limits.

Identifies many possible causes for an effect or problem and sorts ideas into useful categories. It is a visual tool that categorizes potential reasons for defects or issues, typically laid out resembling a fishbone.