Road construction analysis using regression technique
Information Technology Department, Pamantasan Ng Lungsod Ng Maynila, Manila, Philippines.
Research Article
World Journal of Advanced Research and Reviews, 2023, 18(03), 658–664
Article DOI: 10.30574/wjarr.2023.18.3.1125
Publication history:
Received on 03 May 2023; revised on 09 June 2023; accepted on 12 June 2023
Abstract:
Estimating cost in construction is important to the city's design and planning management hence, the construction cost estimate must not be overpriced which may cause corruption or underpricing that leads to unreliable or low-quality road projects. The total estimated cost is only valid in the same year it was proposed because of the inflation rate the costs may change. The researchers applied Multiple Linear Regression technique in predicting total estimated cost for road construction analysis. The model is evaluated by the means of R-squared to determine the variables if they are correlated or overfitting. The calculated R-squared is equals to 0.696598 with the predictor variables (x1 & x2) Roadbed width and Net length and it means that the predictors (Xi) explain 69.7% of the variance of Y. The higher the R-squared result, the better fit it is for the Multiple Linear Regression model. It also shows that X1 and X2 are significant predictor variables. The coefficient of multiple correlation (R) is equals to 0.834624 and it means that there is a very strong correlation between the predicted data and the observed data whereas the dependent variable (y) is the Estimated cost.
CCS CONCEPTS: Multiple Linear Regression • Construction Estimation • Engineering
Keywords:
Machine Learning; Supervised Learning; Prediction; Forecasting
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0