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Note: statistical significance is set at the 5% level. Linear multiple regression test results on percentage error.

Table 6 Note: statistical significance is set at the 5% level. Linear multiple regression test results on percentage error.

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Empirical evidences of project characteristics affecting quality of the estimate
Empirical evidences of project characteristics affecting quality of the estimate
Mean bias using different estimating targets. Table 3 Please cite this article as: Azman, M.A., et al., The accuracy of preliminary cost estimates in Public Works Department (PWD) of Peninsular Malaysia, Internation Journal of Project Management (2012), http://dx.doi.org/10.1016/j.ijproman.2012.11.008
Mean bias using different estimating targets. Table 3 Please cite this article as: Azman, M.A., et al., The accuracy of preliminary cost estimates in Public Works Department (PWD) of Peninsular Malaysia, Internation Journal of Project Management (2012), http://dx.doi.org/10.1016/j.ijproman.2012.11.008
Initial analysis on project characteristics. Table 5
Initial analysis on project characteristics. Table 5
Bias parameter according to different estimating targets. Note: L=lower value, U=upper value Table 4
Bias parameter according to different estimating targets. Note: L=lower value, U=upper value Table 4
Table 7 Note: PI=price intensity. The groups of PI have a different interval values ir order to ensure the group sizes are almost balance.
Table 7 Note: PI=price intensity. The groups of PI have a different interval values ir order to ensure the group sizes are almost balance.
Kolgomorov—Smirnov test on standardized residual.
Kolgomorov—Smirnov test on standardized residual.
Fig. 1. Scatter plot shows bias (%) against number of bidders. There are bias differences between state to state (location) which is similar to the findings by Ogunlana and Thorpe (1991) and Harvey (1979). According to Pegg (1984), the different prices for labor and materials in each location have caused the bias. ANOVA test shows high impact (effect size) of state variable. Malacca and Negeri Sembilan are grouped in the same location (refer to Table 10). However, the finding shows that the differences between these two states are statistically significant. Although Peninsular Malaysia is small, there is a local economic impact on
Fig. 1. Scatter plot shows bias (%) against number of bidders. There are bias differences between state to state (location) which is similar to the findings by Ogunlana and Thorpe (1991) and Harvey (1979). According to Pegg (1984), the different prices for labor and materials in each location have caused the bias. ANOVA test shows high impact (effect size) of state variable. Malacca and Negeri Sembilan are grouped in the same location (refer to Table 10). However, the finding shows that the differences between these two states are statistically significant. Although Peninsular Malaysia is small, there is a local economic impact on
Fig. 2. Mean of the bids above estimate against number of bidders.
Fig. 2. Mean of the bids above estimate against number of bidders.
States grouping for locality index adjustments in the year 2009 Abstracted from Public Works Department, 2009. Please cite this article as: Azman, M.A., et al., The accuracy of preliminary cost estimates in Public Works Department (PWD) of Peninsular Malaysia, Internatior Journal of Project Management (2012), http://dx.doi.org/10.1016/j.ijproman.2012.11.008 Table 10
States grouping for locality index adjustments in the year 2009 Abstracted from Public Works Department, 2009. Please cite this article as: Azman, M.A., et al., The accuracy of preliminary cost estimates in Public Works Department (PWD) of Peninsular Malaysia, Internatior Journal of Project Management (2012), http://dx.doi.org/10.1016/j.ijproman.2012.11.008 Table 10
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EconomicsPublic AdministrationQuantity SurveyingConstruction ManagementConstruction EconomicsConstruction Project ManagementConstruction ProcurementCost EstimationConstruction Cost ModellingCOST ESTIMATION IN PROJECT MANAGEMENT
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