145 units.Ī regression analysis was computed to determine whether the level of depression, level of stress, and age predict the level of happiness in a sample of 99 students (N = 99). In other words, if the level of depression increases for one unit, the level of happiness will decrease by. In our example, unstandardized coefficient B for depression is negative, so we can say that level of depression negatively predicts the level of happiness. Unlike standardized coefficients, which are normalized unit-less coefficients, an unstandardized coefficient has units and a ‘real-life’ scale.įinally, It represents the amount of change in a dependent variable Y due to a change of 1 unit of independent variable X. Unstandardized coefficients are ‘raw’ coefficients produced by regression analysis when the analysis is performed on original, unstandardized variables. 05, so age does not significantly predict the DV. 05, so the stress does not significantly predict happiness. The table shows that the level of depression is p =. 05, then the independent variable does not significantly predict the dependent variable, on the contrary, the IV significantly predicts the DV. Thus, increasing one's discipline will also improve performance.How to report Regression Analysis in SPSS Output? Meanwhile, the significance of Discipline (X2) of 0.001 <0.05, it can be concluded that the Discipline (X2) partially significant effect on employee performance (Y).
Thus, increasing the competence of a person it will also improve performance. In this section displayed significant value Competence (X1) of 0.013 <0.05, then the appropriate basis for decision making in the regression analysis can be concluded that the Competence (X1) partially significant effect on employee performance (Y). Or in other words, Competence (X1) and Discipline (X2) simultaneously significant effect on employee performance (Y) Therefore the probability (0.000) is much smaller than 0.05, then the multiple regression models can be used to predict the performance of employees. In this section displayed a probability level of significance value of 0.000. This suggests the notion that performance (Y) is influenced by 61.6% by Competence (X1) and Discipline (X2), while the rest (100% -61.6% = 38.4%) is explained by other causes. In this section display the value of R = 0.785 and the coefficient of determination (Rsquare) of 0.616. Interpretation of Results of Multiple Linear Regression Analysis Output The last step click Ok, after which it will appear SPSS output, as follows Will display box Linear Regression, then enter Competence and Discipline to box Independent (s), then insert Performance into the box Dependentĥ. Next, from the SPSS menu click Analyze - Regression - linearĤ. Then, click the Data View, and enter the data competence, Discipline and Performanceģ. Furthermore, definition studies variables so that the results fit the picture below.Ģ. Turn on the SPSS program and select the Variable View. Step-by-Step Multiple Linear Regression Analysis Using SPSSġ. Furthermore, the manager collects data competence, Discipline, and Performance of 40 samples of employees. In this case the competence is X1 and X2 is the discipline, while the performance is Y. If the value of Significance 0.05, then the independent variable has no significant effect on the dependent variableĬase in Multiple Linear Regression AnalysisĪ company manager wants to know whether there is an influence of Competence and Discipline of the Employee Performance.Classical assumption does this because independent variables studied amounted to more than one.ĭecision-making process in Multiple Linear Regression Analysis To test multiple linear regression first necessary to test the classical assumption includes normality test, multicollinearity, and heteroscedasticity test. Method Multiple Linear Regression Analysis Using SPSS | Multiple linear regression analysis to determine the effect of independent variables (there are more than one) to the dependent variable.
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