Regression -- as a process -- is an analytical look back through past events (that relate to one another) to find or calculate a smooth path or relationship that more or less represents an optimum way through the events (or dots, or data).
Optimum in this situation means that the "best" regression path is that path for which the distance or divergence from the regression path to any particular event is as minimum as possible.
Regression as strategy
One interpretation of the regression path that is useful for project managers is that the regression path is the overall strategic path, and the events or dots or points off the path are the tactical outcomes in the moment.
The tactical outcomes pull on the strategic path to go this way and that way; thus, the overall strategic outcome is dependent on the tactical outcomes .... but we would expect that consequence.
Other stuff that matters
One more event: Regression analysis can provide valuable insights, such as determining the strength and direction of the direction the strategy might take with one more event pulling this way or that way.In the image above, if the big dot in the upper right is the desired strategic outcome, then some adjustments to tactics are going to be required to bend the trend line to the desired target position.
This situation is the project management challenge: how to use the insight from regression analysis to fashion a path to the target.
A bit about forecasting: Technically the regression line is not valid outside the observed events. Thus: be wary of any projected outcome based exclusively on historical performance. This is the bane of earned value calculations which project linearly based on historical performance.That all said, consider these ideas:
Patterns and Trends: Regression analysis can identify patterns, trends, and relationships in historical data. These patterns can provide insights into future behavior , assuming that the underlying conditions and relationships remain stable. In projects, that assumption is always being tested.
Sensitivity Analysis: Regression analysis allows for sensitivity analysis, which involves examining how changes in the tactics impact the strategy. Sensitivity analysis is also another name for "efficiency" analysis wherein you look for how efficient resources are being used, and what is the marginal value of the next increment of resource.
Some caution, repeated: It's important to note that regression analysis relies on certain assumptions and limitations. Extrapolating relationships beyond the range of observed data or assuming that historical patterns will continue unchanged in the future can introduce uncertainties and errors. Therefore, when using regression analysis for future predictions, it is crucial to consider the limitations and exercise caution while interpreting the results.
Bottom line: Yes, it is possible to conceptualize the optimum path in regression analysis as a strategic outcome and the push-and-pull drivers as tactical inputs. This analogy can be helpful in understanding how regression analysis relates to strategic and tactical decision-making.
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