Using control chart techniques, determine control-chart limits for averages and ranges for Autopitch.

When set at the standard position, autopitch can throw hard balls

Control chart techniques can be used to determine control-chart limits for averages and ranges for Autopitch. A control chart is a graphical representation of statistical data over time, showing how the process changes and reacts to inputs or interventions. The key factors in determining control-chart limits are the number of samples taken, the subgroup size, and the selected distribution (e.g., normal or log normal).

The first step when using control charts is to define the parameters of your sample set: the sample size (n), which should be large enough that it accurately reflects your whole population; and the subgroup size (m). The appropriate subgroup size will depend on what type of analysis you plan to perform. For example, if you are looking at mean values then m should be small enough so that you have a sufficient number of samples in each group without skewing your results. If you are doing range analysis then m should be larger so that there is sufficient variation between groups. Once these parameters have been defined, one can calculate average values (μ) and standard deviations (σ) for both average and range measurements by taking multiple samples from the process being studied. These calculated parameters can then be used to generate upper/lower limit lines based on various confidence intervals like 3 sigma or 6 sigma limits depending on ones desired level of accuracy/confidence in predicting future performance within given bounds. By plotting actual values over time along with these generated lines one can easily monitor how their process is performing relative to expectations over time as well as detect any abnormal behavior which may indicate problems with their system either due to external influences or faults within their own processes.