Data Collection Method The Influence of Working Capital Management and Liquidity Towards Profitability (Case Study: Automotive and Components Industry Listed in Indonesia Stock Exchange 2008-2012)

46 multicollinearity in the regression model can be seen from the value of tolerance and the variance inflation factor VIF. Multicollinearity views of the tolerance value 0.10 or VIF 10. Both of these measurements indicate each independent variable which is explained by the other independent variables. c. Autocorrelation Test Autocorrelation is correlation between observed members arranged in time series if the data used is time series data or correlation among four contiguous variables Andriyatno, 2010. Diagnose the autocorrelation done through testing to test the value of Durbin Watson DW test by Ghozali2009:100.Here the criteria for testing autocorrelation. 1 If 0Dw DL there is any positive autocorrelation. 2 If DL Dw Du or 4-Du D 4-DL uncertain conclusion. 3 If 0 Dw DL or Du Dw 4-Du there is no autocorrelation. 4 If 4-DL Dw 4 there is any negative autocorrelation. d. Heteroscedasticity Test According to Ghozali 2009, the aim of heteroscedasticity 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 homocedasticity andif it different called heteroscedasticity. A good regression model is homocesdasticity or 47 there is no heteroscedasticity. 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 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 patternwavy, widening and then narrow, then it indicates that there is heteroscedasticity. 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 heteroscedasticity or homocedasticity. 3. Hypothesis Testing a. Multiple Regression Analysis Multiple regression analysis is used to test the effect of two or more independent variables toward the dependent variable Ghozali, 2006. Regression analysis divided into two kinds, simple regression analysis if there is only one independent variable and multiple regression analysis if there are more than one independent variables. Multiple regression analysis can be measured partially indicated by coefficient of partial regression jointly indicated by coefficient of multiple determination or R 2 Indriantoro and Supomo, 2009.