GRASP
Seeing the bigger picture in regression analysis
May 27th, 2026, by
Regression analysis is one of the most widely used statistical methods in research, but for many HDR students (and indeed, many researchers!) it can initially seem a bit overwhelming. Between the different types of regression, the terminology, and all the little details to consider, it is easy to lose sight of the bigger picture. At its core, however, regression analysis is about understanding relationships between variables and using data to answer questions. This post focuses on this broader perspective, but if you would like practical guidance on conducting linear or logistic regression, you may also like to visit our Introduction to statistics module.
Exploring relationships using regression
Research is often concerned with understanding whether one factor is associated with another. For example, does sleep influence academic performance? Does exercise improve mental health? Does advertising affect consumer behaviour?
While simple statistical techniques can be used to explore these kinds of relationships, regression analysis allows multiple factors to be examined at the same time. This is important, because real-world outcomes are rarely influenced by a single variable alone. For example, a study investigating university grades might examine the impact of study habits, attendance, stress levels, employment hours and prior academic achievement. Rather than looking at each of these factors in isolation, regression models provide information about how the factors work together to explain academic performance overall, while also showing how each factor individually relates to performance when the influence of the others is taken into account.
Regression analysis in the real world
Many important findings in fields such as public health, social science and business have relied on regression analysis to better understand complex relationships in data. One famous example comes from the Framingham Heart Study, a long-running cardiovascular study that helped identify major risk factors for heart disease. Rather than examining factors such as smoking, blood pressure or cholesterol in isolation, regression analysis allowed these variables to be considered together within the same model, making it possible to estimate how strongly each factor related to cardiovascular risk while accounting for the influence of the others.
Another well-known example, this time from social science, comes from work by Card, who examined the relationship between college proximity and educational attainment using regression models. His findings suggested that living closer to a college was associated with higher levels of completed education, even after accounting for factors such as parental education, region, and IQ.
Keeping sight of the bigger picture
When thinking about your own regression analysis, the technical details are obviously very important. However, it can sometimes be helpful to step back and consider the broader perspective. What variables should be considered together? Which relationships remain once other factors are taken into account? And how does this help answer the research question? Keeping these questions in mind can support decision making and make regression outputs more meaningful and easier to interpret. After all, the results are most useful when interpreted in context, rather than as isolated numbers.
Sources used
Card, D. (1993). Using Geographic Variation in College Proximity to Estimate the Return to Schooling. NBER Working Paper Series, 4483. https://doi.org/10.3386/w4483
Framingham Heart Study. (n.d.). Framingham Heart Study. https://www.framinghamheartstudy.org/
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