Erasmus University Rotterdam Erasmus School of Economics Master Accounting, Auditing and Control Master's Thesis Accounting, Auditing & Control Successful-Efforts
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Appendix 3 Testing the variablesThe variables discussed in chapter six are tested on normality, homoscedasticity, multicollinearity and linearity. First, the variables are tested on normality. Book value model for cash-expense method Normality    
 
As can be seen above RDint-sc, MAssets_sc and MAssets_abs met the assumption normality. The other variables do not meet this assumption, but the sample is large enough to ignore this assumption by the central limit theorem. Homoscedasticity and linearity: The scaled/ relative variables are tested in chapter six in. The absolute variables are showed in the figures below. 
Book value model for successful-efforts method T Returns ASSBRD
DCAPyrend DCAPnew
RDAM MASSBRD
MDACPyrend MDCAPnew
RDint
Revenu his section provides the data and figures to meet the assumptions. Normality:  
The linearity assumption isn’t met by al variables, but according to Moore et al (2003, 447) a sample between 15 and 40 observation may have same skewness. This criterion is met by all variables because the sample is 38 observations and there is no extreme skewness. Homoscedasticity and linearity For homoscedasticity and linearity the following scatterplots are checked: 
The scatterplots show normality and homoscedasticity. In the first scatterplot there is one outlier. The second plot had several outliers. This is because it are absolute values. The scatterplot do not indicate that the variables do not meet the assumption homoscedasticity and linearity. Earnings model for the cash-expense method T
MNIBRD
RDEXP NIBRD
The returns and control variables are not included in this analysis. These variables are already included in previous analysis. The variables in this analysis met the assumption normality. Only the RDEXP with absolute values do not show a normal distribution. This isn’t a problem, because the sample with 111 observations is large enough to base this assumption on the central limit theorem. Homoscedasticity and linearity: The next assumptions that need to be test are homoscedasticity and linearity. To test those assumptions the scatterplots of the regression models need to be investigated. 
The regression models show homoscedasticity and linearity, so the assumptions are met. Earnings model for the successful-efforts method This section provides the data and figures to meet the assumptions. Normality: 
MRDAM
MNIBRDC RDAM
RDEXP NIBRD
NIBRD
RDAM MNIBRD
MRDEXP MRDAM
The histograms show a normal distribution. There is some skewness but the sample exists of 38 observations. Some skewness is allowed by this sample according to the central limit theorem. The assumption of normality is met. Homoscedasticity and linearity: 
There are some outliers in the scatterplot. Those outliers can be explained by the difference in size of the firms. The variables can be used in regression and the assumptions homoscedasticity en linearity are met. Appendix 4 Population
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