The appropriate quality analysis depends on the measurement type and the subgroup
size.

Real-Time SPC provides the following continuous measures.

- Process Measures
- Continuous measures of the process variables that are set and controlled to produce a product. For example, the temperature of the oven in a curing process.
- Output Measures
- Continuous measures of the product. For example, the diameter of a ball bearing.

Continuous data are often collected in subgroups. This means that more than one item is
collected at the same time to represent the process.

- Subgroup size = 1
- When the subgroup size = 1, the appropriate control chart to use is the I-MR chart.
- Subgroup size > 1
- When the subgroup size > 1, the default control chart is the Xbar-R chart. You can change the default control chart type when the subgroup size is > 1 according to your analysis goals.
- Use an Xbar-S chart to monitor the mean and variation of a process when you have continuous data and subgroup sizes of 9 or more.
- Use an I-MR-R/S chart to monitor the mean of your process and the variation between and within subgroups when each subgroup is a different part or batch.
- Use an EWMA chart, which is a time weighted chart, to detect small shifts in the process mean, without influence by low and high values.

Real-Time SPC provides the following continuous capability analyses.

- Normal Capability Analysis
- Use with the I-MR, Xbar-R, Xbar-S, and EWMA control charts.
- Normal distribution with Box-Cox transformation or Nonnormal Capability Analysis
- Use if your data do not follow a normal distribution.
For more information, go to When to use a nonnormal capability analysis.

- Between/Within Capability Analysis
- Use with the I-MR-R/S control chart.

Use Pareto charts when you have assignable cause and corrective actions to help focus your improvements efforts.

Real-Time SPC provides the following attribute measures.

- Defects
- Attribute measure that represents the presence of a single imperfection. A defect
is countable. A single item can have multiple defects of the same type or different
types. For example, a phone has 5 defects: 3 scratches, 1 chip, and 1 dent.
Poisson data are often used to model an occurrence rate, such as defects per unit.

- Defectives
- Attribute measure that represents whether a product is either defective or
acceptable (not defective). A product may have defects but still be considered
acceptable (not defective).
Binomial data are classified into one of two categories such as pass/fail or go/no-go. Binomial data are often used to calculate a proportion or a percentage, such as the percentage of sampled parts that are defective.

Attribute data are also often collected in subgroups. The choice of attribute control
chart depends on whether you collect defects or defective data and how you want to
represent the data.

- Poisson data - defects
- When you count defects, the default control chart is the C chart and the appropriate capability
analysis is a Poisson Capability Analysis.You can change the default control chart type for your analysis. The main difference between U and C charts is the vertical scale.
- Use a C chart to monitor the number of defects.
- Use a U chart to monitor the number of defects per unit.

- Binomial data - defectives
- When you count defectives, the default control chart is the P chart and the appropriate capability
analysis is a Binomial Capability Analysis.You can change the default control chart type for your analysis. The main difference between P and NP charts is the vertical scale.
- Use a P chart to monitor the proportion of defective items.
- Use a NP chart to monitor the number of defective items.

Use Pareto charts to help focus your improvements efforts. Pareto charts are created for the defects and defectives and when you have assignable cause and corrective actions