1. What are the main causes of project delays in DPWH?
The main causes of project delays in DPWH include financial-related causes, design-related causes, natural/external causes, management-related causes, and construction-related causes. Among these, natural/external causes, such as national calamities and pandemics, are the most significant. Additionally, thirteen factors causing delays are identified by the DPWH Offices, including unfavorable conditions, peace and order situations, road right-of-way issues, and late release of funds. It is crucial to consider these factors in construction cost estimation and contingency planning to ensure timely project completion and budget adherence.
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2. What technique did researchers use for prediction model in Iraq?
Researchers developed a prediction model using the Multiple Linear Regression (MLR) technique combined with Weighted Least Squared (WLS) in Iraq. They used 501 sets of historical data from 2005 to 2015. The cost of 25 items was used for predicting the cost using the MLR model. The regression analysis technique proved its purpose with a degree of accuracy of 98.97%. MLR with WLS is promising for initial project stages with limited data and incomplete information. (Abd et al., 2019) and (Gulden, 2013) support the use of regression analysis for estimating relationships among variables.
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3. What research design is used in this study?
The study employs a quantitative -correlational research design utilizing a predictive method that involves regression analysis. This design is used to identify the predictive relationship between the predictor and the outcome variable, forecast outcomes, costs, consequences, or effects. Specifically, the researchers will use the Machine learning technique utilizing multiple linear regression analysis, which requires statistical analysis skills. Multiple regression is a type of regression where the dependent variable shows a linear relationship with two or more independent variables, and it can also be non-linear when the dependent and independent variables do not follow a straight line.
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4. What is a Confusion Matrix used for in model evaluation?
A Confusion Matrix is used to evaluate a model's performance by showing correctly predicted values and types of errors. It helps determine accuracy, misclassification, specificity, and sensitivity. In the researchers' study, it classified true values of Road Construction Projects data to assess the multiple linear regression model's accuracy. The matrix aids in understanding the model's effectiveness in predicting construction cost materials. The researchers followed three stages: analyzing data correlation, estimating the model, and evaluating its validity and usefulness.
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