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Research Paper | Agriculture and Animal Sciences | Nepal | Volume 8 Issue 6, June 2019

Interpreting the Basic Outputs (SPSS) of Multiple Linear Regression

Chuda Prasad Dhakal

Abstract: Regression analysis is one of the important tools to the researchers, except the complex, cumbersome and the expensive undertaking of it; especially in obtaining the estimates correctly and interpreting them plentifully. We perceive a need for more inclusive and thoughtful interpretation of (in this example) multiple regression results generated through SPSS. The objective of this study is to comprehend and demonstrate the in-depth interpretation of basic multiple regression outputs simulating an example from social science sector. In this paper we have mentioned the procedure (steps) to obtain multiple regression output via (SPSS Vs.20) and hence the detailed interpretation of the produced outputs has been demonstrated. We have illustrated the interpretation of the coefficient from the output, Model Summary table (R2, Adj. R2, and SE) ; Statistical significance of the model from ANOVA table, and the statistical significance of the independent variables from coefficients table. An expansive and attentive interpretation of multiple regression outputs has been explained untiringly. Both statistical and the substantive significance of the derived multiple regression model are explained. Every single care has been taken in the explanation of the results throughout the study to make it a competent template to the researcher for any real-life data they will use. Because every effort has been made to clearly interpret the basic multiple regression outputs from SPSS, any researcher should be eased and benefited in their fields when they use multiple regression for better prediction of their outcome variable.

Keywords: Multiple regression, Regression outputs, R squared, Adj R Square, Standard error, Multicollinearity

Edition: Volume 8 Issue 6, June 2019,

Pages: 1448 - 1452

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