Quality management often 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 relating to a process under investigation. The data can then be used to identify trends that can influence quality, such as the rate of variance in the results of a manufacturing process. Descriptive or inferential analysis of 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 such as frequency distributions, histograms, and inferential statistical analysis approaches such as regression analysis and analysis of variance (ANOVA). Each tool has advantages and disadvantages in their use. As a result, the use of the statistical tool often depends on the specific quality problem being investigated. Descriptive Statistics Tools and Histograms Descriptive statistics provide a description of the central properties of data obtained from observations. In quality management, central properties can provide basic information regarding the amount of deviation from the desired norm, which is a great advantage in using descriptive statistics. For example, descriptive statistics can provide information about the frequency of variance in the desired tolerance that is greater than 10%, with less than 10% being the desired norm. In quality management, the descriptive statistics of greatest interest are central tendency, dispersion and frequency (Madan, 268). In ad...... in the center of the sheet......pes of information. At the same time, the disadvantages of various statistical processes suggest that quality managers should use different approaches to data analysis to ensure that their interpretation of the data is correct. Works Cited Christensen, Eldon, Christine Coombes-Betz, and Marilyn Stein. The Certified Quality Process Analyst's Handbook. Milwaukee WI: Quality Press, 2007.Lighter, Donald and Douglas Fair. Principles and methods of quality management in healthcare. Gaithersburg MD: Aspen Publishing, 2000.Madan, Pankaj. Total quality management. Delhi: Krishna House, 2006. Ryan, Thomas. Statistical methods for quality improvement. Hoboken NJ: John Wiley and Sons, 2011.Tari, Juan, and Vincente Sabater. "Quality tools and techniques: are they necessary for quality management? International Journal of Production Economics, 92.3 (December 2004): 267-270.