• analysis of variance
• chi-squared
• correlation
• factor analysis
• mann-whitney U
• mean square weighted deviation
• Pearson program-moment correlation coefficient
• spearman's rank correlation coefficient
• student's t-test
• time series analysis"

 This page will discuss Statistical Process Control (SPC) from a non-mathematical viewpoint.

The basic theory of SPC is that all processes have some random variation. The goal is to keep the total variation small by getting the process under control so only the random variation remains. This can be done in two ways:

1. SPC using control charts where you tend to collect more detailed data and plot them to determine if the process is within an upper and lower boundary. As long as the process stays within those boundaries the process is said to be under control If the process exceeds the upper or lower boundary, then employees must investigate to determine if the process is:
   • out of control
   • steps have been left out
   • process has improved.
2. Non-Statistical Approach. In this approach, the organization does not use use heavy statistics but simply determines what is a acceptable range for the process to be under control For example the firm may decide that as long as HR can hire employees within 1-3 months with a target of 2 months, this is acceptable. Quarterly or annually the HR department may check how they are ding with this goal. They might takes samples. Although they could use the heavy math involved with SPC, this simpler approach may produce most of the benefits o SPC without the time and need for understanding full blown SPC