Interpreting the Basic Results of Multiple Linear Regression
Keywords:multicollinearity, homoscedasticity, Normality, independence, outliers
Regression analysis is one of the most useful tools for academics, although it is a difficult, time-consuming, and expensive effort, especially when it comes to accurately estimating and properly interpreting data. Researchers believe that the findings of multiple linear regression produced by SPSS require a more inclusive and thoughtful interpretation. This study aims to understand and illustrate the detailed interpretation of fundamental multiple linear regression results using the social science sector. In this paper, researcher describe the processes for using SPSS Version 26 to obtain the results from multiple linear regression, and we also show the detailed interpretation of the results. In the results, Model Summary table, Statistical Significance of the Model from the ANOVA Table, and Statistical Significance of the Independent Variables from the Coefficients Table, researcher illustrate the interpretation of the coefficient from the output, B-value, β-value, t-value, and p-value. The results of multiple regression have been discussed in a thorough and careful manner. The resultant multiple linear regression model’s statistical and substantive significance are discussed. Every effort has been made to ensure that the explanation of the findings throughout the study serves as a competent model for the researchers to apply to any real-world data they may employ. Any researcher using multiple linear regression to more accurately predict their outcome variable should feel at peace and gain from doing so because every effort has been made to properly comprehend the fundamental SPSS multiple linear regression outputs.
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