Classical Assumption Test Board Size, Company Size, Profitability and Leverage on Corporate Social Responsibility Reporting in the Annual Report (Empirical Evidence of Mining Companies Listed in Indonesia Stock Exchange Period 2009-2011)

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3. Classical Assumption Test

a. Multicolinearity Test According to Ghozali 2006:95, the aim from multicolinearity test is to test whether the regression model found a correlation among the independent variables. A good regression model should there is no correlation among independent variables. In this research, to detect the presence or absence of multicolinearity can be done by calculating value of variance inflation factor VIF of each independent variable. If the variance inflation factor VIF greater than 10, Ho is rejected that there is multicolinearity, otherwise if variance inflation factor VIF less than 10, Ho is accepted that there is no multicolinearity. b. Heteroskedastisity Test According to Ghozali 2006:125, the aim from heteroskedastisity test is to test whether the regression model occur the variance inequality of the residual from one observation to another observation. If the variance from residual of one observation to other observations is fixed, it is called homokedastisity and if it different called heteroskedastisity. A good regression model is homokedastisity or there is no heteroskedastisity. In this study, heteroskedastisity test can be viewed with using the chart Scatterplot between the predicted value of dependent variable ZPRED and residual SRESID. Y-axis becomes the axis that has been predicted and the X axis is the residual 60 Y predicted-Y actually that has been in the studentized. Basic for decision-making are as follows: 1 If there is a certain pattern, like dots that are forming a regular pattern wavy, widening and then narrow, then it indicates that there is heteroskedastisity. 2 If there is no clear pattern, as well as the dots spread above and below zero 0 on the Y axis, then it indicates that there is no heteroskedastisity or homokedastisity. c. Autocorrelation Test Autocorrelation test aims to test whether a regression model there is a correlation between data in variable. A good regression model is a regression that is free from autocorrelation. In this research, autocorrelation test is done by using the Durbin-Watson Santoso 2000 cited in Nadya 2011:49. Basis for decision-making autocorrelation test is shown in the following table: Table 3.2 Durbin Watson Autocorrelation Measurement Durbin Watson Conclusion Less than -2 Autocorrelation positive available -2 – 2 There is no autocorrelation Above +2 Autocorrelation negative available 61

4. Multiple Regression Analysis