Quality management frequently uses statistical methods to identify the existence of a quality problem and to analyze the root cause of the problem. Statistical methods require the collection of numerical data related to a process under investigation. The data can then be used to identify trends that can affect quality such as the rate of variance in the outcomes of a production process. The descriptive or inferential analysis of the statistical methods can also provide information about the most likely causes of the problem. Statistical methods also have predictive value because they can identify potential problems before they have a significant impact on quality (Ryan, 3).
Some of the statistical tools include descriptive statistics data such as frequency distributions, histograms, and inferential statistics analysis approaches such as regression analysis and analysis of variance (ANOVA). Each tool has advantages and disadvantages to their use. As a result, the use of the statistical tool often depends on the specific quality problem under investigation.
Descriptive Statistics Tools and Histograms
Descriptive statists provide a description of the the central properties of data obtained from observations. In quality management, the central properties can provide basic information concerning the amount of variance from a desired norm, which is a major advantage of using descriptive statistics. For example, descriptive statistics can provide information about the frequency of variance in desired tolerance that is greater than 10%, with less than 10% as the desired norm. In quality management, the descriptive statistical data that is of greatest interest is the central tendency, the dispersion, and the frequency (Madan, 268). In addition to the advantage of providing an initial indication of variance in a process, the descriptive statistical data can provide an estimate of the possible error in a statistical analysis. For example if the mean is substantially different from the median and the mode measures in descriptive statistics, it is possible that the sample may not be normal and is skewed. A disadvantage of descriptive statistics is its sensitivity to outlier scores in small samples, particularly in the mean. In addition, descriptive statistics may provide only limited information about the relationships among variables and causes.
The histogram is one of the basic quality control tools that provide information about processes (Tari and Sabater, 271). It is a bar chart that shows the relative frequency of observations in several classes. In effect, it provides a visual presentation of descriptive statistical frequency data. Figure 1 is an example of a histogram that depicts the relative frequency of an occurrence.
One of the advantages of the histogram is its ability to show whether the data in the different classes or categories is distrusted symmetrically (Ryan, 14). In Figure 1, the data is distributed symmetrically, which approximately a...