**Statistical Analysis Tools And Techniques** – Quality control data visualization tools are a set of tools and techniques that display quality control data in a graphical format. The 6th edition of PMBOK has grouped all tools and techniques into six main categories. Data representation is one of these types. The quality control process also uses other types of tools such as data collection and data analysis. Therefore, this post covers all four essential quality control data display tools.

The main difference between quality assurance and quality control is; Quality assurance is applied during the project planning and implementation phase. In addition, quality assurance ensures that stakeholder requirements are met.

## Statistical Analysis Tools And Techniques

On the other hand, quality control is applied during the project implementation and closure stages. Quality control formally demonstrates with reliable data that sponsor and/or customer acceptance criteria are met.

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Quality control is the process of monitoring and reporting the performance results of quality activities. This is primarily done to evaluate performance and recommend necessary changes to project deliverables.

The quality control process helps identify the causes of poor process or product quality and recommends actions to eliminate them. It also helps to confirm that project deliverables meet the requirements specified by key stakeholders for final acceptance.

The quality control process uses a set of tools to verify that the output provided meets the requirements.

The quality control process uses three different groups of tools and techniques such as data collection, data analysis and data display. In addition, this process uses other tools that are not included in the new structure of tools and techniques.

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Quality control data presentation tools help to present data in a visual format. Data visualization connects data analysis and its results. Visual representation also helps to clearly and concisely convey the result of the data analysis process. The following paragraph lists four quality control data display tools.

The first tool for displaying quality control data is the cause and effect diagram. Fishbone / Ishikawa or why-why diagram is an alternative name for this qualitative tool.

The “head” of the fish bone has the problem statement. This is the starting point for finding the source of the problem and its practical cause. A problem statement usually describes a process gap or quality objective.

Causes are discovered by looking at the question statement and asking “why” until an actionable root cause is identified or until reasonable possibilities are exhausted.

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Fishbone diagrams are often useful in linking the expected unintended effects of specific changes to the attributable cause that requires corrective action by project teams to eliminate specific changes identified in the control chart.

Histogram is the second quality control data display tool. A histogram is a special form of bar chart that describes the central tendency, spread, and shape of a statistical distribution.

Histograms summarize measured data on a continuous scale and show the frequency distribution of some qualitative characteristic (statistically, central tendency and data distribution).

Unlike a control chart, a histogram does not consider the effect of time on changes in distribution.

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A third tool for displaying quality control data is a control chart. A control chart is a time trend chart that determines whether a process is stable or not. It also indicates whether a process has predictable performance.

Each control chart has a centerline, statistical control limits, and control data. Some control cards also have specification limitations. The median line is a solid line representing the average or arithmetic mean of the score measurements. There are two statistical control limits:

Standard statistical calculations and principles determine control limits. Control limits ultimately determine the natural ability of a process to stabilize. Upper and lower control limits are different from specification limits. Upper and lower specification limits are based on contract requirements. They reflect the maximum and minimum values allowed for a process.

The project manager and appropriate stakeholders can use statistically calculated control limits to identify points to initiate corrective action to prevent abnormal process performance.

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Typically, +/-3 standard deviations around the process mean indicate the control limits of a repeatable process. A standard deviation of zero indicates the mean value of the process. The following conditions determine whether a process is out of control:

Control charts can track various output variables that are commonly used to track the repetitive activities required to produce pieces of product.

However, control charts can also examine cost and schedule changes. Additionally, control charts can control the volume and frequency of amplitude changes. Additionally, control charts can be used for other management outcomes to determine whether project management processes are in control.

A fourth quality control data display tool is the scatter plot. A scatter plot is also called a correlation plot. These graphs attempt to explain the change in the dependent variable (Y) relative to the change in the corresponding independent variable (X).

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The direction of correlation can be proportional (positive correlation), inverse (negative correlation), or the correlation pattern is absent (zero correlation).

The existence of correlation between the dependent and independent variables facilitates the creation of a regression line. This regression line estimates how a change in the independent variable (X) affects the value of the dependent variable (Y).

In summary, the quality control data visualization tools mentioned above are general data analysis tools that can help the project manager solve critical problems. These tools are also part of the seven key quality control tools. To learn more about the seven key quality control tools, read the post below.

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The blog contains essential resources for project management professionals and pmp enthusiasts. See resources for more information In this stage of the information society, a more important issue is how to regulate the cognitive process, taking into account uncertainty and ambiguity, which are the inherent characteristics of uncertainty. This has theoretical and practical implications in fields such as technology, economics, and biomedicine. Real events are actually the main source of inspiration for this type of management. Information and uncertainty are two closely related concepts from a theoretical point of view. Lack of information leads to uncertainty.

On the other hand, information can be considered to reduce uncertainty, but this is only one possibility, although this is a significant perspective on the idea. Consequently, any conceptual expansion of the information field is accompanied by the need to manage new forms of uncertainty. Uncertainty theory has greatly expanded its conceptual scope and methodological tools due to the combined effect of two developments in mathematical thought over the past few decades. On the one hand, using the classical theory of additive measures (such as probability measures) for uniform non-additive measures (such as probability measures, confidence functions, interval-valued probabilities). On the other hand, the application of classical set theory in the study of fuzzy sets is in two ways, standard and non-standard.

Statistical reasoning is a method of reasoning in the context of uncertainty and incomplete knowledge. The above developments in information and uncertainty have naturally influenced statistics. Widespread fundamental theory of fuzzy sets in logic, mathematics, and engineering has provided statistical methods with exciting ideas and new tools. Since the late 1960s, a steady stream of contributions has extended statistical reasoning to include fuzzy data and fuzzy uncertainties. However, these developments have been somewhat hampered by contributions from different scientific communities and different drivers ranging from small to large problems (from control systems to medical diagnostics, from marketing to environmental studies). Given these contributions, there is a strong desire to systematize statistical reasoning, and this special issue aims to take a step forward in this direction. To define the overall significance of this endeavor, we need a broader framework that accommodates both classical statistical information and uncertainty concepts, as well as new ones developed from the fuzzy approach in its broadest sense.

The most basic concept in the theory of fuzzy sets is a fuzzy set T in a specific reference set (the world of discourse) defined by a mapping.

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Consider when observed statistical data refer to vague, poorly defined, or linguistic labels for ambiguous concepts (good, big, etc.). A convenient approach to deal with such data is to “fuzzy” them by creating appropriate “fuzzy value variables” that can express the uncertainty/redundancy associated with each observation. For example, we refer to the incommensurate situation in the following sections. However, the development of this example can easily be extended to multiple scenarios. To formally define the stochastic process that gives rise to a random variable with an imprecise value in the probability domain, a measurement requirement must be given. This condition is made in such a way that the regularization guarantees that the concepts of random variable and random set are extended.

Another important contribution of “fuzzy thinking” to statistical analysis is how to create statistical models. The focus of accuracy in this case is on theoretical information components such as parameters or other model properties. In this context, fuzzy clustering and fuzzy regression analysis are two prominent examples.

The so-called “fuzzy inference

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