Degrees of freedom is a parameter used to help select the critical value in some probability distributions such as a T-test, f-test, Analysis of Variance (ANOVA), chi-square, etc. It is beyond the scope of the course to describe exactly what degrees of freedom is, but suffice it to say that it is the number of paired comparisons necessary to attain a critical value.
Descriptive statistics describe data collected. Measures of central tendency, such as mean and median, and measures of dispersion such as standard deviation and range, are used to summarize and interpret some of the properties of a data set (e.g., sample, or subgroup) are known as descriptive statistics. Descriptive statistics can actually be verified from the data provided. Example: Of the citations for speeding issued in July by Officer Hunt, 23% were given to drivers of red cars. This can be verified by looking at Officer Hunt’s July citation record.
Continuous data can be measured on a continuum. Think of it as being able to divide a measure by one half, and in half again, and in half again, – to infinity. Contrast continuous data with discrete data where there are only a finite number of values possible or if there is a space on the number line between two possible values. For example, it’s impossible to roll the 2.3 on a roll of the dice. You can either roll a two or three but nothing in between.
First, the team defines a problem in the define phase – the project charter, customer needs and requirements, and they developed a process map. The next phase is the measurement phase where the team develops a data collection plan and establishes a baseline sigma. Following the measurement phase is the analyze phase where the team performs data analysis, process analysis, and root-cause analysis. Next is the improvement phase where the team generates a host of possible solutions. They select a solution and then ultimately implement the solution. Finally, in order to maintain the solution, the team develops a control plan and a response plan as a part of the control phase of DMAIC.
The effort/impact matrix (also known as the impact/effort matrix) is used to help the team to decide which of the numerous solutions to implement appear to be the easiest (least effort) while having the most favorable impact.
The F-distribution is another distribution that is used extensively in statistical analysis to test certain hypotheses arising from the comparison of two or more normal distributions. One of the ‘operational definitions’ of quality follows: Quality is considered the minimum deviation around a target value. We often spend a significant amount of time and money making sure our processes are ‘targeted’ appropriately, but it is just as important to assure that processes are operating as consistently (that is, with as little variation) as possible.
A factorial experiment is the simultaneous manipulation of factors at different levels to see their effect on some desired result. A factorial experiment could be ‘a full factorial’ or it could be a ‘fractional factorial’ experiment. If the experiment is fractionated, the practitioner gives up something in the sense that main effects are aliased with interactions, or interactions are aliased with other interactions, and so forth. A full fractional factorial experiment has none of the aliasing, but it cost more because you have to run more experimental trials.
A flowchart (also known as a process flow diagram) is a graphical tool that depicts distinct steps of a process in sequential order (from top to bottom of the page). The basic idea is to include all of the steps of critical importance to the process. Also, flowcharts are often annotated with performance information.
The alternate hypothesis is the complement of the null hypothesis. The null hypothesis is what you anticipate through randomness. The alternative hypothesis, sometimes known as the alternate hypothesis is the opposite of that. It is what you would not anticipate by randomness. More often than not you are trying to reject the null because you are trying to see a change in something – considering that most six sigma projects are trying to fix broken processes.
An interrelationship diagram shows graphically the cause-effect relationships that exist among a group of items, issues, problems, or opportunities. It is particularly useful in helping to identify the potential causal relationships that might lie behind a problem that continues to reoccur despite attempts to resolve it.
