Variables sampling plan
In statistics, a variables sampling plan is an acceptance sampling technique. Plans for variables are intended for quality characteristics that are measured on a continuous scale. This plan requires the knowledge of the statistical model (e.g. normal distribution). The historical evolution of this technique dates back to the seminal work of W. Allen Wallis (1943). The purpose of a plan for variables is to assess whether the process is operating far enough from the specification limit. Plans for variables may produce a similar OC curve to attribute plans with significantly less sample size. The decision criterion of  these plans are where formula_3 and formula_4 are the sample mean and the standard deviation respectively, formula_5 is the critical distance, formula_6 and formula_7 are the upper and lower regulatory limits. When the above expression is satisfied the proportion nonconforming is lower than expected and therefore the lot is accepted. A variables sampling plan can be designed so that the OC curve passes through two points (AQL,formula_8) and (LQL,formula_9). AQL and LQL are the Acceptable quality limit and the limiting quality level respectively. formula_8 and formula_9 are the producer and consumer's risks. The required sample size (formula_12 ) and the critical distance (formula_5) can be obtained as where formula_15 is the normal distribution function. When the dispersion is known the required sample size (formula_12) is obtained from while for unknown formula_4 the sample size is approximately The provides tables to obtain the required sample size and the critical distance according to the type of inspection. External links. OC curve