Statistical quality control is one of the four main types of quality control measures used in the assessment of statistical methods. All types of statistical quality control are based on the assumption that the distribution of a variable is normally distributed and can be analyzed using the appropriate statistical software. When this assumption is violated, some type of error can occur, and it can have serious statistical impact. For example, an estimator using a formulae based on historical data might come up with a range that is too high or too low, a result of improper specification of the formulae. Other cases might be sampling errors, such as failing to reject the null hypothesis (i.e. a significant result is found to be insignificant) or failing to conduct reliable sub-analysis when the range of values is too large.
An example of statistical quality control measure used in this article is the R value of the log-likelihood function. The log-likelihood function minimizes the difference between the mean and the log of the predicted value. This function is actually a mathematical equation, where the assumptions of a normal distribution and linearity are taken into account. The log-likelihood function has been called the ‘Lagrange point’ since it was discovered that if there exists such a relationship then the Lagrange limit (the set of ideal points at which the mean value of the log-likelihood function would lie) tends to be very similar to the size of the range of points deviated from the mean. The closest solution to such points is known as the corresponding LLS, or Lesser Lagueness, and is equivalent to the statistical error of about 0.5 per cent.
The various types of statistical quality control charts are based on the graphical presentation of the data. These include graphs, histograms, and scatter plots. Some charts display the data in a format that can be easily understood by the audience such as bar charts, line charts, or pie charts. Sometimes the data is presented in a more complicated format such as tree-ring plots showing density distributions of variables over time, average values, or other types of graphical presentations intended to convey a message.
Quality assurance is based upon the conclusion of statistical tests. This conclusion is reached when the testing is performed and the statistical significance is determined. Quality control begins with the decision to perform a statistical test. Once the testing is completed, the results are reported in an unqualified report that either the manager or an outside statistician may use to evaluate the quality of the company’s statistical system. This decision to use a statistical test is often based upon a company’s perception of its statistical method, which may be based upon prior experience with the type of statistical test being used, or on a company’s intuition about the range of values that could be obtained through statistical analysis.
There are many statistical quality control processes that managers use in the selection of statistical testers and in ensuring that the statistical testers are following the standards of the company. Managers are responsible for ensuring that the statistical test is properly written and that it is comprehensive. Additionally, managers are responsible for selecting and ensuring that the statistical tester is trustworthy and has an acceptable record of reliability and performance. In addition, the manager should monitor the progress of the statistical quality assurance process to ensure that it continues to meet the requirements laid forth.
The effectiveness of the statistical quality assurance process is dependent upon the selection of a reliable statistical test. A reliable statistical test must meet the requirements of the statistical agency that issued the test. In addition, the test must have been accepted by the statistical community at large. Finally, the test must have been subjected to statistical analysis to show its validity before the conclusions are drawn.
Many companies focus only on statistical quality control. This does not take into account the non-statistical aspects of quality control, such as error management. Companies that have a solid statistical process also have a higher level of quality than companies that do not take into consideration the non-statistical aspects of quality control. The results of a statistical test could mean the difference between success and failure of a business or a company in particular.
The statistical quality control process within a company is not solely based upon the results of the statistical test itself. It is important that the manager be alert for any indications of statistical instability or inconsistency. The manager must have the resources to address these issues before the problems become significant. The identification of these issues can come from many sources, such as performance monitoring, periodic review, interviews with management and staff, and from a company’s SOP. These measures of quality control are important for the overall success of a business and can provide a great benchmark to compare against.