Interpretation of Regression in Spss
Interpretation:
* MODEL SUMMARY Model Summary | Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | 1 | .549a | .301 | .292 | .59246 | a. Predictors: (Constant), MEAN_OC | The first table of interest is the Model Summary table. This table provides the R and R2 value. * The R value is 0.549, which represents the simple correlation. * It indicates a average degree of correlation. The R2 value indicates how much of the dependent variable, "Job Satisfaction", can be explained by the independent variable, "Organizational Commitment" or how they depend on each other. * In this case, 30.1% can be explained, which is very small or they both are little bit depends on eachother. |
* ANOVA
ANOVAa | Model | Sum of Squares | df | Mean Square | F | Sig. | 1 | Regression | 11.784 | 1 | 11.784 | 33.572 | .000b | | Residual | 27.378 | 78 | .351 | | | | Total | 39.162 | 79 | | | | a. Dependent Variable: MEAN_JS | b. Predictors: (Constant), MEAN_OC |
ANOVA TABLE * This table indicates that the regression model predicts the outcome variable significantly well. * Here, p(sig.) < 0.0005, which is less than 0.05, and indicates that, overall, the model applied can statistically significantly predict the outcome variable. * Or we can say that the “organizational commitment” significantly predict the “Job Satisfaction”.
* Coefficients
Coefficientsa | Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | | B | Std. Error | Beta | | | 1 | (Constant) | 1.633 | .352 | | 4.642 | .000 | | MEAN_OC | .520 | .090 | .549 | 5.794 | .000 | a. Dependent Variable: MEAN_JS |
Coefficients, provides us with information on each predictor variable. * This gives us the information we need to predict “Job Satisfaction” from “Organizational commitment”. * We can see that both the constant and “Organizational commitment” contribute significantly to the model (by looking at the Sig. column). *
* MODEL SUMMARY Model Summary | Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | 1 | .549a | .301 | .292 | .59246 | a. Predictors: (Constant), MEAN_OC | The first table of interest is the Model Summary table. This table provides the R and R2 value. * The R value is 0.549, which represents the simple correlation. * It indicates a average degree of correlation. The R2 value indicates how much of the dependent variable, "Job Satisfaction", can be explained by the independent variable, "Organizational Commitment" or how they depend on each other. * In this case, 30.1% can be explained, which is very small or they both are little bit depends on eachother. |
* ANOVA
ANOVAa | Model | Sum of Squares | df | Mean Square | F | Sig. | 1 | Regression | 11.784 | 1 | 11.784 | 33.572 | .000b | | Residual | 27.378 | 78 | .351 | | | | Total | 39.162 | 79 | | | | a. Dependent Variable: MEAN_JS | b. Predictors: (Constant), MEAN_OC |
ANOVA TABLE * This table indicates that the regression model predicts the outcome variable significantly well. * Here, p(sig.) < 0.0005, which is less than 0.05, and indicates that, overall, the model applied can statistically significantly predict the outcome variable. * Or we can say that the “organizational commitment” significantly predict the “Job Satisfaction”.
* Coefficients
Coefficientsa | Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | | B | Std. Error | Beta | | | 1 | (Constant) | 1.633 | .352 | | 4.642 | .000 | | MEAN_OC | .520 | .090 | .549 | 5.794 | .000 | a. Dependent Variable: MEAN_JS |
Coefficients, provides us with information on each predictor variable. * This gives us the information we need to predict “Job Satisfaction” from “Organizational commitment”. * We can see that both the constant and “Organizational commitment” contribute significantly to the model (by looking at the Sig. column). *