Quality Control Charts Startup Panel - Variables Tab

Select the Variables tab of the Quality Control Charts Startup Panel to access the options described here.

SixGraph with X-bar & R chart. Select SixGraph with X-bar & R chart for variables to start this analysis, which produces a SixGraph compound graph and an X-bar chart (for means) and an R (range) chart for the variability of the process.

SixGraph with X-bar & S chart. Select SixGraph with X-bar & S chart for variables to start this analysis, which produces a SixGraph compound graph and an X-bar chart (for means) and an S (standard deviation) chart for the variability of the process.

SixGraph with X & MR chart. Select SixGraph with X-bar & R chart for variables to start this analysis, which produces a SixGraph compound graph and an X (for individuals) chart and an MR (moving range) chart for the variability of the process.

X-bar & R chart for variables. Select X-bar & R chart for variables to start this analysis, which produces an X-bar chart (for means) and an R (range) chart for the variability of the process.

X-bar & S chart for variables. Select X-bar & S chart for variables to start this analysis, which produces an X-bar chart (for means) and an S (standard deviation) chart for the variability of the process.

MA X-bar & R chart for variables. Select MA X-bar and R chart for variables to start this analysis, which produces an MA chart. In this chart, a moving average of means (or individual observations) across a specified number of successive samples is plotted, and control lines are established around that moving average line. This is useful for detecting small permanent shifts (trends) in the process average. Note that this chart can be constructed for individual observations (N=1), in which case sigma will be estimated from moving ranges.

MA X-bar & S chart for variables. Select MA X-bar and S chart for variables to start this analysis, which produces an MA chart. In this chart, a moving average of means (or individual observations) across a specified number of successive samples is plotted, and control lines are established around that moving average line. This is useful for detecting small permanent shifts (trends) in the process average.

EWMA X-bar & R chart for variables. Select EWMA X-bar and R chart for variables to start this analysis, which produces an EWMA chart. Instead of plotting the simple average of a particular number of successive sample means, these means are weighted so that historically "older" means are assigned increasingly smaller weights. This is useful for detecting small permanent shifts (trends) in the process average. Note that this chart can be constructed for individual observations (N=1), in which case sigma will be estimated from moving ranges.

EWMA X-bar & S chart for variables. Select EWMA X-bar and S chart for variables to start this analysis, which produces an EWMA chart. Instead of plotting the simple average of a particular number of successive sample means, these means are weighted so that historically "older" means are assigned increasingly smaller weights. This is useful for detecting small permanent shifts (trends) in the process average.

Individual & moving range. Select Individual & moving range to start this analysis, which produces Individual & moving range charts. In this plot individual observations (sample size of 1) are plotted and you use so-called moving ranges (of adjacent observations) in order to estimate a process sigma and establish control limits. Note that MA and EWMA charts can also be constructed for individual observations, in which case sigma will also be estimated from moving ranges.

CUSUM chart for individuals. Select CUSUM chart for individuals to start this analysis, which produces a CUSUM (cumulative sum) chart for individuals. These charts are useful for controlling trends or minor mean shifts in a variable related to quality. Note that STATISTICA computes the recommended tabular or algorithmic CuSum chart, and not the "old-style" V-mask control limits that were commonly in use when these charts were (literally) made by hand. See Montgomery (1996, Chapter 7) for details and recommendations.

Pareto Chart Analysis. Select Pareto Chart Analysis to start this analysis, which produces a Pareto chart. You can plot a histogram by category, where the bars in the histogram are sorted in descending order. Such plots allow for the easy identification of the primary sources (categories) of quality problems.

See also, Common Types of Charts, Control Charts for Variables vs. Charts for Attributes, Quality Control Index, and Technology of the STATISTICA Quality Control Charts Module (Technical Note).