Communicating the findings of a linear regression analysis involves presenting the estimated coefficients, their statistical significance, the goodness-of-fit of the model, and relevant diagnostic information. For example, one might state the regression equation, report the R-squared value, and indicate whether the coefficients are statistically significant at a chosen alpha level (e.g., 0.05). Presenting these elements allows readers to understand the relationship between the predictor and outcome variables and the strength of that relationship.
Clear and concise presentation of statistical analyses is crucial for informed decision-making in various fields, from scientific research to business analytics. Effective communication ensures that the findings are accessible to a broader audience, facilitating replication, scrutiny, and potential application of the results. Historically, standardized reporting practices have evolved to enhance transparency and facilitate comparison across studies, contributing to the cumulative growth of knowledge.
