Correlation and Regression Analysis Essay Sample

Correlations
 Long term LitigationAmount of compensation
Long term LitigationPearson Correlation1-.295*
Sig. (2-tailed) .038
N5050
Amount of compensationPearson Correlation-.295*1
Sig. (2-tailed).038 
N5050
*. 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 
ModelRR SquareAdjusted R SquareStd. Error of the Estimate 
1.094a.015.125.716 
a. Predictors: (Constant), Number of injuries 
ANOVAa 
ModelSum of SquaresdfMean SquareFSig. 
1Regression.2201.220.430.515b 
Residual24.60048.512   
Total24.82049    
a. Dependent Variable: Amount of compensation 
b. Predictors: (Constant), Number of injuries 
Coefficientsa
ModelUnstandardized CoefficientsStandardized CoefficientstSig.
BStd. ErrorBeta
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
ModelRR SquareAdjusted R SquareStd. Error of the Estimate
1.073a.050-.3744.427
a. Predictors: (Constant), Amount of compensation, Number of injuries
ANOVAa 
ModelSum of SquaresdfMean SquareFSig. 
1Regression4.90622.453.125.883b 
Residual921.1144719.598   
Total926.02049    
a. Dependent Variable: Profitability 
b. Predictors: (Constant), Amount of compensation, Number of injuries 
Coefficientsa
ModelUnstandardized CoefficientsStandardized CoefficientstSig.
BStd. ErrorBeta
1(Constant)12.4312.195 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.

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