In regression modeling, which aspect is often considered subjective?

The choice of variables in regression modeling is often considered subjective because it involves the analyst's decision-making based on their understanding of the data, the context of the study, and the underlying theory. Variables can be selected based on prior research, theoretical frameworks, or even intuition. Analysts may have different perspectives on which variables are relevant or how they should be operationalized, leading to a level of subjectivity in the selection process.

Moreover, variable selection can significantly impact the model's outcomes, and different analysts might prioritize different aspects of the data, such as focusing on variables that are conceptually important, statistically significant, or practically relevant. This decision-making process often lacks a formal, objective criterion and can be influenced by various interpretations, experiences, and the specific hypotheses being tested.

In contrast, the selection of the type of regression used, the determination of the significance level, and the selection of confidence intervals generally rely more on established statistical principles, established criteria, or formulas rather than subjective judgment. While there can be some flexibility or interpretation in each of those areas, they are typically guided by standard practices or statistical theory.