Regression Analysis-Based Cost Estimation of Simulation Models in Model Cost Database

2016 ◽  
Author(s):  
Hae Young Lee ◽  
So Jin Lee ◽  
Hyung-Jong Kim ◽  
SeungHyun Byun
Keyword(s):  
Regression Analysis ◽  
Cost Estimation ◽  
Simulation Models

Equations for postnatal chemical composition of the fat-free empty body of sheep and cattle

1983 ◽  
Vol 100 (3) ◽  
pp. 693-699 ◽  
Author(s):  
T. W. Searle ◽  
D. A. Griffiths
Keyword(s):  
Computer Simulation ◽  
Weight Loss ◽  
Regression Analysis ◽  
Compensatory Growth ◽  
Simulation Models ◽  
Published Data ◽  
Simple Description ◽  
Positive Growth ◽  
Computer Simulation Models ◽  
The Relationship

SUMMARYRegression analysis was used to examine the relationship between the weight of water, protein and ash (kg) and fat-free empty body (FFEB) weight (kg) in data from six published and one unpublished experiment conducted in this laboratory. The analysis involved 201 sheep divided into 12 groups which were either in positive growth, weight stasis, weight loss or compensatory growth prior to slaughter. Each of the relationships between water, protein or ash and FFEB was linear and could be expressed by a common slope. Small (though statistically significant) differences in intercept were found between treatments. The following overall equations provide a simple description of the data:water (kg) = 0·721 FFEB + 0·37, R.S.D. 0·18,protein (kg) = 0·215 FFEB - 0·22, R.S.D. 0·16,ash (kg) = 0·055 FFEB - 0·07, R.S.D. 0·09.Published data for cattle were also examined and corresponding equations are:water (kg) = 0·701 FFEB + 3·59, R.S.D. 2·6,protein (kg) = 0·234 FFEB - 2·00, R.S.D. 2·3,ash (kg) = 0·060 FFEB - 0·71, R.S.D. 1·8.It is suggested that these equations could be used in computer simulation models of growth to calculate FFEB gain (or loss) from nitrogen balance.

scholarly journals The Analysis of Cost Estimation using Cost Significant Model on Bridge Construction in South Sulawesi

2021 ◽  
Vol 921 (1) ◽  
pp. 012073
Author(s):  
E Aprianti ◽  
S Hamzah ◽  
M A Abdurrahman
Keyword(s):  
Regression Analysis ◽  
Cost Estimation ◽  
Construction Projects ◽  
Steel Frame ◽  
Bridge Construction ◽  
South Sulawesi ◽  
The Cost ◽  
Total Project Cost ◽  
Total Project ◽  
Significant Model

Abstract One of the fundamental problems faced by the province of South Sulawesi is the factor of accessibility, so the role of bridges is quite important. For this reason, the budget planning for standard bridge construction projects also needs to be efficient in terms of preparation and accurate in terms of budget. The Cost Significant Model is one of the total construction cost estimation models that relies more on the prices that have the most influence on the total project cost as the basis for estimation. In general, this study uses data from steel frame bridge construction projects in South Sulawesi Province to formulate a mathematical model with linear regression analysis so that it can be used in the process of estimating similar projects going forward. The Estimation Model which is formed from the regression analysis and the Cost Significant Model in this study, namely; Y = 3.884 (X7) + 0.989 (X8) - 65515.372. With; Y = Estimated Total Cost (Rp/m); X7 = Reinforcement Work Cost (Rp/m); X8 = Steel Frame Structure Work Cost (Rp/m). Where this model can explain 99.7% of the total project cost with a cost model factor of 1.038. The level of accuracy (percentage error estimate) of the estimation results of the Cost Significant Model in this study ranges from - 1.46% to +2.45%.

Fitting Parameters to Complex Models by Direct Search

1970 ◽  
Vol 7 (4) ◽  
pp. 503-512 ◽  
Author(s):  
Theodore A. Van Wormer ◽  
Doyle L. Weiss
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Simulation Models ◽  
Direct Search ◽  
Nonlinear Structures ◽  
Empirical Validation ◽  
Search Techniques ◽  
Complex Models ◽  
Fitting Parameters

Empirical validation and testing of simulation models have been hampered by the problems associated with estimating parameters for their nonlinear structures. This article discusses direct search techniques as a methodology for fitting parameeters of models too complex for linear regression analysis.

A Case Study on Cost Estimation and Profitability Analysis at Continental Airlines

2011 ◽  
Vol 26 (1) ◽  
pp. 181-200
Author(s):  
Francisco J. Roma´n
Keyword(s):  
Regression Analysis ◽  
Cost Estimation ◽  
Operating Costs ◽  
Fiscal Year ◽  
Potential Cost ◽  
Cost Drivers ◽  
Profitability Analysis ◽  
Cost Behavior ◽  
Recent Financial Crisis

ABSTRACT: This case exposes students to the application of regression analyses to be used as a tool pursuant to understanding cost behavior and forecasting future costs using publicly available data from Continental Airlines. Specifically, the case focuses on the harsh financial situation faced by Continental as a result of the recent financial crisis and the challenges it faces to remain profitable. It then highlights the importance of reducing and controlling costs as a viable strategy to restore profitability and how regression analysis can assist in this pursuit. Students are next presented with quarterly data for various categories of costs and several potential cost drivers, which they must use to perform regressions on operating costs using a variety of cost drivers. They must then use their regression results to forecast operating costs and conduct a profitability analysis to project quarterly profits for the upcoming fiscal year. Finally, students must summarize the main results of their analysis in a memorandum addressed to Continental’s management, providing recommendations to restore profits. In particular, the concept of mixed cost functions is reinforced, as is the understanding of the steps required to perform regression analysis in Excel, interpreting the regression output, and the underlying standard assumptions in regression analysis. The case has been tested and well received in an intermediate cost accounting course and it is suitable for both undergraduate and graduate students.

Improving accuracy of conceptual cost estimation using MRA and ANFIS in Indonesian building projects

2018 ◽  
Vol 8 (4) ◽  
pp. 348-357 ◽  
Author(s):  
Dwifitra Jumas ◽  
Faizul Azli Mohd-Rahim ◽  
Nurshuhada Zainon ◽  
Wayudi P. Utama
Keyword(s):  
Regression Analysis ◽  
Cost Estimation ◽  
Fuzzy Inference ◽  
Early Stage ◽  
State Building ◽  
Building Projects ◽  
Inference System ◽  
Content Type ◽  
Proposed Model ◽  
West Sumatra

Purpose The purpose of this paper is to develop a conceptual cost estimation (CCE) model for building project by using a pragmatic approach, which is a mix of tools drawn from multiple regression analysis (MRA) and adaptive neuro-fuzzy inference system (ANFIS), to improve the accuracy of cost estimation at an early stage. Design/methodology/approach This paper presents a set of MRA and integrating MRA with ANFIS or MRANFIS. A simultaneous regression analysis was developed to determine the main cost factors from 12 variables as input variables in the ANFIS model. Cost data from 78 projects of state building in West Sumatra, Indonesia were used to indicate the advantages of the proposed model. Findings The result shows that the proposed model, MRANFIS, has successfully improved the mean absolute percent error (MAPE) by 2.8 percent from MRA of 10.7–7.9 percent for closeness of fit to the model data and by 3.1 percent from MRA of 9.8–6.7 percent for prediction performance to the new data. Research limitations/implications Because the significant variables are different for each building type, the model may be not appropriate for other buildings depending on the characteristics of building. The models can be used and analyzed based on the own historical project data for each case so that the model can be applied. Originality/value The study thus provides better accuracy of CCE at an early stage for state building projects in West Sumatra, Indonesia by using the integrated model of MRA and ANFIS.

scholarly journals Cost Estimation Process of Remote Sensing Satellites

2021 ◽  
Vol 11 (2) ◽  
pp. 78-85
Author(s):  
Dechamma K K ◽  
◽  
Mohith C G ◽  
Suma Mirji ◽  
Rahul Kumar ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Regression Analysis ◽  
Cost Estimation ◽  
Deterministic Model ◽  
Knowledge Building ◽  
Cost Model ◽  
Technical Knowledge ◽  
Building Relationships ◽  
Technical Parameters ◽  
The Cost

Forecasting cost of satellites is not a recent development in space agencies, they were in practice from the beginning using traditional methods. The attempt to make it simpler, quicker and accurate; established the path to build a model by incorporating statistics, technology and technical knowledge. Building relationships between satellite cost and the technical parameters affecting them directly or indirectly became the basis of the model. The building of the cost model is more vexing than it looks. It requires data to perform regression analysis, which can be linear or nonlinear along with transformations. This paper also specifies the significance of the uncertainty impacting the cost associated with the technical parameters and the method of estimation. The overall model is mapped into three parts; the manpower and facility cost model being the deterministic bottom-up model and the combination of probabilistic and deterministic model for satellite cost.

scholarly journals Comparison of the Performance of “Unit Area Cost Method” (UACM) and “Unit Price Method” (UPM) Used in Estimating the Costs of the Reinforced Concrete Multi-Storey Housing Buildings

2021 ◽  
Author(s):  
Latif Onur Uğur ◽  
◽  
Kadir Penbe
Keyword(s):  
Regression Analysis ◽  
Reinforced Concrete ◽  
Error Rate ◽  
Cost Estimation ◽  
Unit Area ◽  
Linear Regression Analysis ◽  
Estimation Method ◽  
Error Rates ◽  
Unit Price ◽  
The Cost

Unit Price Method (UPM) and Unit Area Cost Method (UACM) are widely used in the cost of reinforced concrete multi-storey housing buildings. In this study, it is aimed to determine the cost estimation method with high performance (its use will provide an advantage to the estimator over the other) by comparing the cost estimates in the modeling by making “Regression Analysis” (RA), with the data of such struction. In the literature review, studies of equivalent and different structures were evaluated. In modeling; Number of rooms, floor area, total area, number of floors, floor height, facade area, facade void area, Bathroom/wc areas, balcony areas, building height are parameters. UPM and UACM based costs which were created with the data of 2020 of 41 similar structures (38 for modelling, 3 for tests) were used as independent variables, and cost models were created with linear regression analysis. The results were randomly selected and compared with test groups that were not used in these models, and the error rates and performances of the methods were tested. According to the comparison, in the UACM analysis, there was a high R2 value in 6 data and a low error rate in 8 predictions; In the UPM analysis, it was determined that an equally high R2 value and a low error rate occured in 7 predictions. As a result, UACM reached a better performance in finding the estimated cost; It has been observed that using it in cost estimation gives better results. However, even if UACM performed better, the difference in error rates is very low, at 2.7%.

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