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.