Quantitative Methods and Analysis - Essay Example

It is expected that increase in employee benefits will be positively related with intrinsic, extrinsic and overall job satisfaction. What are the least squares regression line equations for each of the 3 different regressions? The regression line is the basic simple linear regression model: linear in the parameter ?0 (or Y-intercept) and ?1 (Slope); and in the independent variable (Kutner, Nachtsheim and Neter, 2004). Mathematically, simple linear regression is represented as: Y = ?0 + ?1(X) This paper drew data from the AIU data set.

Regression analysis was carried out by excel. Three regression lines were obtained by using benefit as an independent variable and intrinsic, extrinsic and overall job satisfaction as dependent variables. Following results were obtained. Regression Line 1: Intrinsic Job Satisfaction = 4.61781924 + 0.034(Benefits) Regression Line 2: Extrinsic Job Satisfaction = 5.411102 - -0.058 (Benefits) Regression Line 3: Overall Job Satisfaction = 4.934424 + 0.006301(Benefits) What are the slopes and the y-intercepts?

‘ Regression Line 1:   Coefficients Intercept 4.61781924 Slope 0.033893373 Regression Line 2:   Coefficients Intercept 5.411102 Slope -0.058 Regression Line 3:   Coefficients Intercept 4.934424 Slope 0.006301 What are the R-squared values for the 3 different regressions? Regression Line 1: Regression Statistics R Square 0.001964739 Regression Line 2: Regression Statistics R Square 0.011193 Regression Line 3: Regression Statistics R Square 0.000174 Similarities, differences and strength of correlation The results obtained by the regression analyses revealed both positive and negative slope values showing positive and negative relationships between the dependent and the independent variables.

The first and third regression lines have a positive slope which shows that employee benefits are positively related with intrinsic and overall job satisfaction. However, small values of R-square show a weak relationship between these variables. The second regression model shows negative correlation between benefits and extrinsic job satisfaction. The strength of this relationship as measured by R square was highest and therefore the correlation between these two variables was high (Stuart, 1998) Conclusion The purpose of this paper was to examine the relationship between employee benefits and job satisfaction.

It was asserted that increase in employee benefits would be positively related with intrinsic, extrinsic and overall job satisfaction. However, the results of this study weren’t consistent with this assertion. Overall, the results of this study show that the employee benefits can be used to predict employee satisfaction. According to the results employee benefits were positively related with intrinsic and overall job satisfaction. Furthermore, there was negative relationship between benefits and extrinsic job satisfaction.

The strength of this relationship as measured by R square was highest and therefore the correlation between these two variables was high. References Kutner, M. H.. Nachtsheim, C. J and Neter J. (2004), Applied Linear Regression Models, 4th ed., McGraw-Hill/Irwin, Boston Locke, E. A. (1976). Nature and causes of job satisfaction. In M.D. Dunnette, (Ed.), Handbook of industrial and or Organizational psychology, 1297-1349. Chicago. Schneider, B., Gunnarson, S. K.,& Wheeler, J.K. (1992). The role of opportunity in the conceptualization