The cause and effect diagram is used to identify and discover all the potential or real causes (or inputs) that will lead to or result in a single effect (or output). The arrangement of the causes is done according to their level of importance or detail which results in a depiction of relationships and hierarchy of events.

It helps in finding the root causes, identifying  the problematic areas and comparing the relative importance of different causes. It is also used to the organization diagramming, parts hierarchies, project planning, tree diagrams, and the 5 Why’s.

The C&E diagram is also famous as the fishbone diagram because it resembles the skeleton of a fish, with the main causes categories drawn as “bones” attached to the spine of the fish.

Chi-square test

A chi-square test (also chi-squared or ?2  test) is any statistical hypothesis test  used to compare observed data with data we would expect to obtain according to a specific hypothesis and in which the sampling distribution of the test statistic is a chi-square distribution when the null hypothesis is true.

Chi-square test  practices null hypothesis, which states that there is no significant difference between the expected and observed result in an event.

Some examples of chi-squared tests are Pearson’s chi-square test, Yates’ chi-square test, Mantel-Haenszel chi-square test, Linear-by-linear association chi-square test, The portmanteau test in time-series analysis, testing for the presence of autocorrelation.

Control Charts

The control chart, also known as the Shewhart chart or process-behaviour chart, is a tool used in statistical process control for determining  whether a business process is in a state of statistical control or not. It is also known as a special-cause variation.

All points will be plotted within the control limits in case the process is in control,. Any observations outside the limits suggests  the introduction of a source of variation which is new and unanticipated.

As increased variation means increased quality costs, a control chart revealing  the presence of a special-cause calls for immediate investigation. Thus the control limits are a crucial decision aids. The control limits suggests about process behaviour .

Six sigma tools are the Quality Management techniques, approaches and methodologies used in Six Sigma for the process improvement in an organization. Appropriate implementation of these tools brings about amazing results.

In the beginning of a new process, the effective application of these tools and techniques ensures successful implementation of Six Sigma Quality initiatives.

Six Sigma uses  a great number of established quality management tools and methods which are applied not only in Six Sigma projects but also outside applications as well.

Affinity Diagram

The objective for the development of affinity diagram was discovering meaningful groups of ideas within a raw list. It emphasize on allowing the groupings emerge naturally by using the right side of the brain. Basically it is used to refine a brainstorm into something which can be dealt more easily and that makes sense.

Ishikawa recommends using the affinity diagram in variety of situations like when facts or thoughts are unorganized and not certain, to overcome preexisting ideas or paradigms, to clarify the ideas  and when unity within a team is required.

Analysis of Variance

Analysis of variance (ANOVA) is a statistical term which defines the collection of statistical models with their  associated procedures. Here the observed variance is partitioned into components because of different explanatory variables. Simply, ANOVA produces a statistical test of whether the means of several groups are all equal

Basically, there are three conceptual classes of statistical models:

Fixed-effects models (Model 1): It  is based on the assumption that  the data coming from normal populations which are differing  only in their means.

Random effects models (Model 2) : It is based on the assumption that the description of data reveals a hierarchy of different populations whose differences are constrained by the hierarchy.

Mixed-effect models(Model 3): It explains the scenarios where there is presence of  both fixed and random effects.

Usually  there are several types of ANOVA depending on the number of treatments and the way they are applied like one way Anova,  Factorial Anova, mixed design Anova.