To understand the statistical quality control methodologies used in manufacturing, one must have a basic understanding of the statistical methodologies and concepts used in different disciplines like engineering, chemistry, medicine and biology. The purpose of this article is to identify various grinding process parameters that affect the performance of bearings in the process of machine tools by employing statistical concepts in the area of machine tools manufacturing.

An inspection of the performance results of each of the finished components under different combinations of mechanical process factors is also examined. It is found that the performance results vary greatly from individual to individual and from machine to machine tool. When a variation in performance from one part of a set of components to another is due to differences in the characteristics of the component, it is termed as an occurrence. On the other hand, when variations in performance from different machines are due to differences in the characteristics of each machine, it is known as a variation. In order to make use of statistical concepts in the area of machine tools manufacturing, it is necessary to identify all these occurrences and their causes.

The first thing that can be done is the implementation of statistical quality control. This can be done using quality assurance measures like quality management systems, quality control measures, quality control training, quality control methods and quality control systems. Another effective way of performing statistical quality control is through a statistical analysis of statistical data.

Statistical analysis of statistical data is mainly concerned with the collection, processing, analysis and reporting of statistical data. Statistics refers to the ratio of value to quantity. It is considered as the scientific study of changes in the probability and frequency of occurrences over a definite period of time and space. As such, a statistical analysis is concerned with the frequency and sequence of events that take place in a certain condition. The events that are taken into consideration in statistical analysis are those that have a significant relationship with the quantitative variables of a certain condition.

Statistical analysis is carried out to find out causes and predictors of changes in the probability of occurrence of the events. There are different types of statistical analysis that are applied in order to prove the statistical significance of different occurrences. Some of these include statistical correlation analysis, linear regression, probability distribution, chi-square or R-squared, Bayesian analysis and the logistic model. Other statistical approaches are also applied, such as the binomial and Poisson distributions. and the Fisher-transient distribution.

In order to analyze the statistical significance, the data used in statistical significance is compared to a pre-determined set of pre-determined data. These data sets may include data obtained in previous tests for quantitative variables like the percentage of failures of an operation, the percentage of defects in an assembly or the probability of a product failure to complete its life cycle. This set of data is called a criterion set. A test for statistical significance is an investigation that involves a single test or multiple tests performed on the data obtained.

Statistical significance is determined by comparing the test results against the criterion set. If there is no significant difference between the results of these two tests, the data is classified as the null hypothesis, which is considered as the value of statistical significance.

The other way to determine statistical significance is by conducting several tests of different significance and then comparing them to the pre-determined set of data. This is known as the multiple hypothesis test procedure. However, the method used to make a conclusion is not always the same for different types of studies, so it becomes essential to make use of a statistical significance analyzer, which is capable of performing multiple tests on the data obtained to determine the significance of a particular hypothesis.