Summary of Important Points
Table 82 provides the highlights of this chapter.
Table 82: 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 r^{2} = 1, then the regression line is "perfectly" fitted to the data observations; if r^{2} 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 outoftolerance 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 onetime endeavor never to be exactly repeated and Six Sigma is a strategy for repeated processes.

Six Sigma stresses highquality 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 buildto and buyto 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.
