For a long time, I have been pushing for a better understanding of regression. Regression gives us insight into the multivariate relationship that exists in the world. It is difficult to visualize these relationships as the number of dimensions can exceed human imagination. For all that complexity though, regression is an ancient concept (by measure of the speed at which new techniques come into the analytics industry). Why has it not been adopted to understand the world a lot more?
I think due to the complexity in visualizing these relationships, there is resistance to using these ideas. I am adamant that people who show me anything think along those lines. There is a chance that some of these insights can be developed based on individual analyses slowly. It will be a challenge to ensure that you can highlight everything.
While people look at regression to tell us what will happen, I believe regression is a tool that is best used for measurement. The more complex the relationship, the more important is to ensure that we get the measurement framework right. The measurement of the impact of engine size on mileage is straightforward, but the measurement of the marketing spend on TV on its impact on sales is not so easy. Due to significant relationships between may contributing factors, teasing out the impact of TV marketing spend is a challenge that marketers have tried to solve with no easy solution. While these aspects might pose challenges to using a regression framework, there are a couple of other places where regression may be misleading.
1. When we have non-linear temporal relationships, a straightforward regression approach will not measure relationships accurately leading to misleading diagnoses.
2. When there is a feedback loop, regression usage might even lead to counter-intuitive relationships. While these relationships may not be difficult to recognize, they need to be measured with other techniques to get the right perspective.
I think due to the complexity in visualizing these relationships, there is resistance to using these ideas. I am adamant that people who show me anything think along those lines. There is a chance that some of these insights can be developed based on individual analyses slowly. It will be a challenge to ensure that you can highlight everything.
While people look at regression to tell us what will happen, I believe regression is a tool that is best used for measurement. The more complex the relationship, the more important is to ensure that we get the measurement framework right. The measurement of the impact of engine size on mileage is straightforward, but the measurement of the marketing spend on TV on its impact on sales is not so easy. Due to significant relationships between may contributing factors, teasing out the impact of TV marketing spend is a challenge that marketers have tried to solve with no easy solution. While these aspects might pose challenges to using a regression framework, there are a couple of other places where regression may be misleading.
1. When we have non-linear temporal relationships, a straightforward regression approach will not measure relationships accurately leading to misleading diagnoses.
2. When there is a feedback loop, regression usage might even lead to counter-intuitive relationships. While these relationships may not be difficult to recognize, they need to be measured with other techniques to get the right perspective.
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