# Summary of Important Points

## Summary of Important Points

Table 8-2 provides the highlights of this chapter.

Table 8-2: Summary of Important Points

Point of Discussion

Summary of Ideas Presented

Regression analysis

• Regression analysis is a term applied by mathematicians to the investigation and analysis of the behaviors of one or more data variables in the presence of another data variable.

• The primary outcome of regression analysis is a formula for a curve that "best" fits the data observations.

• In regression analysis, one or more of the variables must be the independent variable.

• Statistical methods aim to minimize the distance or error between the probabilistic values and the real value of the variable.

• If r2 = 1, then the regression line is "perfectly" fitted to the data observations; if r2 is 0, the regression curve is less representative of the relationship between X and Y.

Hypothesis testing

• The Type 1 error is straightforward: the hypothesis is true but we reject or ignore the possibility.

• The Type 2 error is usually less risky: we falsely believe the alternate hypothesis and make investments to protect against the outcome that never happens.

• We have to establish an interval around the likely outcome, called the interval of acceptance, within which we say "good enough."

• Regardless of the actual distribution, over a very large number of trials the average outcome distribution will be Normal.

• A common test in hypothesis testing is to discover the true mean of the distribution for H(0) and H(1) using a statistic commonly called the "t statistic."

Risk management with P * I

• The project manager's objective is to filter the list and identify the risks that have a prospect of impacting the outcome of the project. For this filtering task, a common tool is the P * I analysis, or the "probability times impact" analysis.

• Risk under management (dollar value) = (Risk \$value * Risk probability).

• The average of the risks under management will be Normal or approximately so.

Six Sigma

• Six Sigma's goal is to reduce the product errors experienced by customers; in other words, to improve the quality of products as seen and used by customers.

• In the Motorola process, the process mean is allowed to drift up to 1.5σ in either direction, and the process random effects should stay ±3σ from the mean at all times.

• The confidence that no outcome will occur out of tolerance is such that only 3.4 out-of-tolerance outcomes (errors) will occur in either direction for every 1 million opportunities.

• The differences between project management and Six Sigma arise from the fact that a project is a one-time endeavor never to be exactly repeated and Six Sigma is a strategy for repeated processes.

• Six Sigma stresses high-quality repeatability that plays well with the emerging maturity model standards for project management.

Quality function deployment

• QFD is about deployment of project requirements into the deliverables of the WBS by applying a systematic methodology that leads to build-to and buy-to specifications for the material items in the project.

• QFD is a process and a tool.

• The process is a systematic means to decompose requirements and relate those lower level requirements to standards, metrics, development and production processes, and specifications.

• The tool is a series of interlocked and related matrices that express the relationships between requirements, standards, methods, and specifications.

• Achieving a useful QFD result requires validation of results by business managers and by other subject matter experts.