FORECASTING CONCEPTUAL COSTS OF BRIDGE PROJECTS USING NON-PARAMETRIC REGRESSION ANALYSIS

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Yuanxin Zhang ◽  
R. Edward Minchin
Keyword(s):  
Regression Analysis ◽  
Cross Validation ◽  
Model Performance ◽  
Multivariate Adaptive Regression Splines ◽  
Parametric Regression ◽  
State Highway ◽  
Novel Approach ◽  
Analysis Technique ◽  
Construction Documents ◽  
Non Parametric

The cost of bridge construction is influenced by numerous internal and external factors, making it very difficult to approximate. Years prior to a bridge project letting, state highway agencies (SHAs) must set a reliable budget for the proposed project, but the information available to them is very limited. Developing reliable cost estimates for bridge projects during the early pre-construction phases is very important and challenging for SHAs. This study employed a non-parametric regression analysis technique—multivariate adaptive regression splines (MARS)—to model the conceptual cost of bridge projects. This novel approach does not require detailed construction documents and does not require strict assumptions to be valid for the developed model or to be reliable for the model predictions. MARS was applied to the empirical data gathered from a Florida Department of Transportation database. The 10-fold cross-validation method was employed in this study to assess model performance. The criteria to gauge overall model fit, generalizability, and prediction error were evaluated. The results revealed that the developed model consistently performed well, based on Cross-Validated R-square (CVRSq), Generalized Cross-Validation (GSV), Generalized R-square (GRSq), and max error.

LOCATION OF PARTICULAR POINTS IN NON-PARAMETRIC REGRESSION ANALYSIS

1995 ◽  
Vol 37 (2) ◽  
pp. 161-168 ◽  
Author(s):  
Joel Boularan ◽  
Louis Ferre ◽  
Philippe Vieu
Keyword(s):  
Regression Analysis ◽  
Parametric Regression ◽  
Non Parametric

scholarly journals Approach of Analysis of Data Mining Prediction In Earthquake Case Using Non Parametric Adaptive Regression Method

2020 ◽  
pp. 247-253
Author(s):  
Dadang Priyanto ◽  
Muhammad Zarlis ◽  
Herman Mawengkang ◽  
Syahril Efendi
Keyword(s):  
Data Mining ◽  
Regression Analysis ◽  
Regression Function ◽  
Regression Method ◽  
Multivariate Adaptive Regression Splines ◽  
Linear Regression Method ◽  
Ground Acceleration ◽  
Independent Variables ◽  
Adaptive Regression ◽  
Non Parametric

Data Mining is the process of finding certain patterns and knowledge from big data. In general, the data mining process can be grouped into two categories, namely descriptive data mining and data mining prediction. There are several Math functions that can be used in the data mining process, one of which is the Classification and Regression function. Regression Analysis is also called Prediction analysis, which is a statistical method that is widely used to investigate and model relationships between variables. Regression analysis to estimate the regression curve can be done by analyzing Nonparametric Regression. One well-known method in non-parametric regression is MARS (Multivariate Adaptive Regression Spline). The MARS method is used to overcome the weaknesses of the Linear Regression method. The use of a stepwise backward algorithm with the CQP quadratic programming framework (CQP) from MARS resulted in a new method called CMARS (Conic Multivariate Adaptive Regression Splines). The CMARS method is able to model high dimensional data with nonlinear structures. The flexible nature of the CMARS model can be used in the process of analyzing earthquake predictions, especially in Lombok, West Nusa Tenggara. Test results Obtained a mathematical model of four independent variables gives significant results to the dependent variable, namely Peak Ground Acceleration (PGA). Contributions of independent variables are the distance of the epicenter 100%, magnitude 31.1%, the temperature of the incident location 5.5% and a depth of 3.5%.

Pengaruh Kepemimpinan dan Disiplin Kerja Terhadap Kinerja Karyawan: Studi Kasus PT Sucofindo Palembang Indonesia

MBIA ◽  
2019 ◽  
Vol 18 (1) ◽  
pp. 76-84
Author(s):  
Muhammad Idris ◽  
Dian Novita Sari
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Random Sampling ◽  
Linear Regression Analysis ◽  
Secondary Data ◽  
Multiple Linear Regression Analysis ◽  
Primary Data ◽  
Collection Method ◽  
Analysis Techniques ◽  
Analysis Technique

The problem in this study is whether there is an influence of leadership and work discipline on the employees’ performance of PT.Sucofindo Palembang City. This research includes associative research. The sample in this study were 88 respondents, with propotionate random sampling analysis technique. The data used were primary data and secondary data. Data collection method through questionnare. Analysis techniques using multiple linear regression analysis, F test (Simultaneoys) and t test (partial) and determination coeffiecient. The results show that there is influence of leadership and work discipline on the performance of PT.Sucofindo Palembang City.

THE EFFECT OF REPUTATION AND COMPETENCE ON CUSTOMER LOYALTY THROUGH CUSTOMER SATISFACTION

2020 ◽  
pp. 66-72
Author(s):  
Ari Dwi Astono ◽  
Widji Astuti ◽  
Harianto Respati
Keyword(s):  
Higher Education ◽  
Regression Analysis ◽  
Customer Satisfaction ◽  
Customer Loyalty ◽  
Sampling Method ◽  
Higher Education Institutions ◽  
Direct Influence ◽  
Tertiary Institutions ◽  
Central Java ◽  
Analysis Technique

This study aims to analyze the effect of reputation, competence on customer loyalty with customer satisfaction as an intervening variable. The population in this study were students of private tertiary institutions in Central Java who are members of Services for Higher Education Institutions Region VI, while a sample of 5 private universities, using the purposive sampling method, was taken with the Slovin formula of 190 respondents. The analysis technique uses regression analysis. Research results show the customer satisfaction variable can be an intervening variable or able to mediate between the direct influence of the reputation variable and the competency variable on customer loyalty variables.

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