Presenting the findings of a multiple regression analysis involves clearly and concisely communicating the relationships between a dependent variable and multiple independent variables. A typical report includes essential elements such as the estimated coefficients for each predictor variable, their standard errors, t-statistics, p-values, and the overall model fit statistics like R-squared and adjusted R-squared. For example, a report might state: “Controlling for age and income, each additional year of education is associated with a 0.2-unit increase in job satisfaction (p < 0.01).” Confidence intervals for the coefficients are also often included to indicate the range of plausible values for the true population parameters.
Accurate and comprehensive reporting is vital for informed decision-making and contributes to the transparency and reproducibility of research. It allows readers to assess the strength and significance of the identified relationships, evaluate the model’s validity, and understand the practical implications of the findings. Historically, statistical reporting has evolved significantly, with an increasing emphasis on effect sizes and confidence intervals rather than solely relying on p-values. This shift reflects a broader movement towards more nuanced and robust statistical interpretation.
