Categories
College Essay Examples

Correlation and Regression Analysis Essay Sample

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.

Avatar photo

By Hanna Robinson

Hanna has won numerous writing awards. She specializes in academic writing, copywriting, business plans and resumes. After graduating from the Comosun College's journalism program, she went on to work at community newspapers throughout Atlantic Canada, before embarking on her freelancing journey.

Leave a Reply

Your email address will not be published. Required fields are marked *

Related Posts