| Correlations | |||
| Long term Litigation | Amount of compensation | ||
| Long term Litigation | Pearson Correlation | 1 | -.295* |
| Sig. (2-tailed) | .038 | ||
| N | 50 | 50 | |
| Amount of compensation | Pearson Correlation | -.295* | 1 |
| Sig. (2-tailed) | .038 | ||
| N | 50 | 50 | |
| *. Correlation is significant at the 0.05 level (2-tailed). |
Hypothesis: There is no relationship between the number of long term litigations and the amount compensation.
Interpretation: The relationship between the number of litigations and amount of compensation is not significant, P value = -.295 >.05. Therefore, the hypothesis is not rejected. The r = 1 indicating a strong positive relationship between long term litigation and the amount of compensation.
Simple regression analysis
| Model Summary | |||||||||||||||||
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | |||||||||||||
| 1 | .094a | .015 | .125 | .716 | |||||||||||||
| a. Predictors: (Constant), Number of injuries | |||||||||||||||||
| ANOVAa | |||||||||||||||||
| Model | Sum of Squares | df | Mean Square | F | Sig. | ||||||||||||
| 1 | Regression | .220 | 1 | .220 | .430 | .515b | |||||||||||
| Residual | 24.600 | 48 | .512 | ||||||||||||||
| Total | 24.820 | 49 | |||||||||||||||
| a. Dependent Variable: Amount of compensation | |||||||||||||||||
| b. Predictors: (Constant), Number of injuries | |||||||||||||||||
| Coefficientsa | |||||||||||||||||
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | |||||||||||||
| B | Std. Error | Beta | |||||||||||||||
| 1 | (Constant) | 2.050 | .196 | 10.452 | .000 | ||||||||||||
| Number of injuries | .022 | .034 | .094 | -.656 | .515 | ||||||||||||
| a. Dependent Variable: Amount of compensation | |||||||||||||||||
Hypothesis: There is no relationship between the number of injuries and the amount compensation.
Interpretation: The results indicate that amount of compensation is positively influenced the number of injuries. Constant is 2.05 indicating variation in performance when the variable is zero; a unit change in the number injuries increase compensation amount by 9.4%.
R square is 12% indicating that number of injuries accounts for 37% changes in the amount of compensation. This is weak of association and does not reflect the extent to which any particular independent variable is associated with the dependent variable.
Multiple Regression analysis
| Model Summary | ||||
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate |
| 1 | .073a | .050 | -.374 | 4.427 |
| a. Predictors: (Constant), Amount of compensation, Number of injuries |
| ANOVAa | ||||||||||||
| Model | Sum of Squares | df | Mean Square | F | Sig. | |||||||
| 1 | Regression | 4.906 | 2 | 2.453 | .125 | .883b | ||||||
| Residual | 921.114 | 47 | 19.598 | |||||||||
| Total | 926.020 | 49 | ||||||||||
| a. Dependent Variable: Profitability | ||||||||||||
| b. Predictors: (Constant), Amount of compensation, Number of injuries | ||||||||||||
| Coefficientsa | ||||||||||||
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||||||||
| B | Std. Error | Beta | ||||||||||
| 1 | (Constant) | 12.431 | 2.195 | 5.662 | .000 | |||||||
| Number of injuries | -.066 | .209 | -.046 | .315 | .754 | |||||||
| Amount of compensation | -.319 | .893 | -.052 | -.358 | .722 | |||||||
| a. Dependent Variable: Profitability | ||||||||||||
Hypothesis: The impact of compensation and number of injuries on financial performance is not significant
Interpretation: The results indicate that performance is negatively related to the amount of compensation and the number of injuries. Constant is 12.4 indicating variation in performance when the variables are zero; a unit change in the number injuries reduces performance by 4.6%; and a unit change in the amount of compensation reduces financial performance by 5.2%.
R square is 37% indicating that the number of litigations and the amount of compensation accounts for 37% changes in company performance. This is weak of association and does not reflect the extent to which any particular independent variable is associated with the dependent variable.
Reference
Field, A. (2005). Discovering stats using SPSS (2nd ed.). London, England: Sage.
