scholarly journals Bridge Construction Cost Prediction using Multiple Linear Regression

2019 ◽  
Vol 8 (9) ◽  
pp. 3115-3121
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Correlation Method ◽  
Multiple Linear Regression Model ◽  
Percentage Error ◽  
Cost Prediction ◽  
Independent Variables ◽  
Qualitative Variables ◽  
Leave One Out

Cost of construction of bridges is predicted using multiple linear regression model, based on data of bridges from Maharashtra state in India. Cost per unit area is taken as an appropriate dependent variable. Using both conventional and double log regression techniques, models for cost/m2 and log of cost/m2 are developed. Total 6 independent variables, which include both qualitative and quantitative variables, are used to develop the model. Height of bridge, average span length and depth of foundation are used as quantitative variables. Zone of construction, deck type and foundation type are used as qualitative variables in developing model. Strength of these independent variables with dependent variable is found out using pearson’s correlation method. Model is then verified using Leave One Out Cross Validation (LOOCV) technique. The most suited regression model obtained from the data experiment is double log regression with R2 of 0.850 and a Mean Absolute Percentage Error (MAPE) of 17.74%, as compared to 25% MAPE observed in past for studies related to traditional cost prediction.

A Mathematical Comparison of the Lachance - Traill Matrix Correction Procedure with Statistical Multiple Linear Regression Analysis in XRF Applications

1992 ◽  
Vol 36 ◽  
pp. 1-10
Author(s):  
Anthony J. Klimasara
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Mathematical Meaning ◽  
Matrix Correction ◽  
Independent Variables ◽  
Xrf Analysis

AbstractIt will be shown that the Lachance-Traill XRF matrix correction equations can be derived from the statistical multiple linear regression model. By selecting and properly transforming the independent variables and then applying the statistical multiple linear regression model, the following form of the matrix correction equation is obtained:Furthermore, it will be shown that the Lachance-Traill influence coefficients have a deeper mathematical meaning. They can be related to the multiple regression coefficients of the transformed system:Finally, it will be proposed that the Lachance-Traill model is equivalent to the statistical multiple linear regression model with the transformed independent variables. Knowing these facts will simplify correction subroutines in Quantitative/Empirical XRF Analysis programs. These mathematical facts have already been implemented and presented in a paper: “Automated Quantitative XRF Analysis Software in Quality Control Applications” (Pacific-International Congress on X-ray Analytical Methods, Hawaii, 1991).This demonstrates that the Lachance-Traill model has a strong mathematical foundation and is naturally justified mathematically.

scholarly journals The Effect of Phase on Paper Thickness Optimization in the Paper Recycling Process Using Recycling Machine

2020 ◽  
Vol 2 (1) ◽  
pp. 19
Author(s):  
Fauzhia Rahmasari
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Multiple Linear Regression Model ◽  
Thickness Optimization ◽  
Recycling Process ◽  
Paper Recycling ◽  
Independent Variables ◽  
The Relationship

AbstractEfforts to manage the recycling of paper waste into new paper have been carried out in recent times. It takes a tool or machine that is able to effectively and efficiently recycle used paper into new paper. There are several factors that affect the effectiveness of paper recycling machines, one of which is the paper thickness. One method that can be used to analyze the factors that influence paper thickness in the paper production process using a paper recycling machine is regression analysis. Regression analysis is data analysis techniques in statistics that is used to examine the relationship between several independent variables and dependent variable. However, if we want to examine the relationship or effect of two or more independent variables on a dependent variable, the regression model used is a multiple linear regression model. This study purposes are to analyze the factors that influence paper thickness using a paper recycling machine using multiple linear regression and to inform the modeling about that. The results showed that the factors that affect the paper thickness optimization are destruction and press phase. AbstractUpaya pengelolaan daur ulang sampah kertas menjadi kertas baru telah banyak dilakukan pada jaman sekarang. Dibutuhkan suatu alat atau mesin yang mampu secara efektif dan efisien dalam mendaur ulang kertas bekas menjadi kertas baru. Terdapat beberapa faktor yang mempengaruhi tingkat efektifitas mesin daur ulang kertas diantaranya adalah ketebalan kertas. Salah satu metode yang dapat digunakan untuk menganalisis faktor-faktor yang mempengaruhi ketebalan kertas pada proses produksi kertas menggunakan mesin daur ulang kertas adalah analisis regresi. Analisis regresi merupakan teknik analisis data dalam statistika yang digunakan untuk mengkaji hubungan antara beberapa variabel bebas dengan variabel tidak bebas. Namun, jika ingin mengkaji hubungan atau pengaruh dua atau lebih variabel bebas terhadap satu variabel tidak bebas, maka model regresi yang digunakan adalah model regresi linier berganda. Tujuan dalam penelitian ini yaitu menganalisis faktor-faktor yang mempengaruhi ketebalan kertas menggunakan mesin daur ulang kertas menggunakan regresi linier berganda serta memberikan informasi pemodelan mengenai hal tersebut. Hasil penelitian menunjukkan bahwa faktor yang mempengaruhi keoptimalan ketebalan kertas adalah fase penghancuran dan pemadatan kertas

The Impact of Climate Change on Agriculture in Morocco: An Econometric Study

2020 ◽  
Vol 1 (2) ◽  
pp. 19-28
Author(s):  
Faycel Tazigh
Keyword(s):  
Climate Change ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Independent Variables ◽  
Temperature And Precipitation ◽  
The Impact ◽  
The Relationship

This paper aims to analyze the relationship that may exist between climate change and cereal yield in Morocco. In order to study this correlation between variables, we used the most common form of regression model which is the multiple linear regression model. There are two main uses of multiple linear regression model. The first one is to quantify the weight of impact that the independent variables had on the dependent variable. The second use is to predict not only the relationship that may found between variables but also their impacts. In our case, we have chosen temperature and precipitation as an independent variables and cereal yield as dependent variable.

FACTORS ASSOCIATED WITH CHILDREN’S HEIGHT AND BODY MASS INDEX IN LITHUANIA, 1990–2008

2015 ◽  
Vol 48 (4) ◽  
pp. 502-529 ◽  
Author(s):  
Andrej Suchomlinov ◽  
Janina Tutkuviene
Keyword(s):  
Body Mass Index ◽  
Linear Regression ◽  
Regression Model ◽  
Socioeconomic Factors ◽  
Multiple Linear Regression ◽  
Birth Order ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Factors Associated ◽  
Independent Variables

SummaryThe aim of the study was to reveal the ethnic and socioeconomic factors associated with height and body mass index (BMI) of children during the period of political and social transition in Lithuania in 1990–2008. Data were derived from the personal health records of 1491 children (762 boys and 729 girls) born in 1990 in Vilnius city and region. Height and BMI from birth up to the age of 18 years were investigated. Children were divided into groups according to their ethnicity, place of residence, father’s and mother’s occupation and birth order. Height and BMI were compared between the groups; a Bonferroni correction was applied. A multiple linear regression model was used to measure the effects of the independent variables on height and BMI. Girls living in Vilnius city were significantly taller in later life at the ages of 8 and 11 years. Sons of mothers employed as office workers appeared to be significantly taller at the ages of 7, 12, 14 and 15 years compared with the sons of labourers. First-born girls were taller at the age of 7 years than later-born girls of the same age (124.48±5.11 cm and 122.92±5.14 cm, respectively,p<0.001). Later-born children of both sexes had higher BMIs at birth compared with first-borns; however, first-born girls had higher BMIs at the age of 11 years compared with their later-born peers (17.78±2.87 kg/m² and 16.79±2.14 kg/m² respectively,p<0.001). In the multiple linear regression model, the five tested independent variables explained only up to 18% of total variability. Boys were more sensitive to ethnic and socioeconomic factors: ethnicity appeared to be a significant predictor of boys’ height at the age of 5 years (p<0.001), while birth order (p<0.001) predicted boys’ BMI at birth. In general, ethnicity, place of residence, father’s and mother’s occupation and birth order were not associated with children’s height and BMI in most age groups.

scholarly journals Forecasting energy consumption in Tamil Nadu using hybrid heuristic based regression model

2019 ◽  
Vol 23 (5 Part B) ◽  
pp. 2885-2894 ◽  
Author(s):  
Karuppusamy Sakunthala ◽  
Salvarasan Iniyan ◽  
Selvaraj Mahalingam
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Energy Consumption ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Tamil Nadu ◽  
Multiple Linear Regression Model ◽  
Percentage Error

Energy consumption forecasting is vitally important for the deregulated electricity industry in the world. A large variety of mathematical models have been developed in the literature for energy forecasting. However, researchers are involved in developing novel methods to estimate closer values. In this paper, authors attempted to develop new models in minimizing the forecasting errors. In the present study, the economic indicators of the state including population, gross state domestic product, yearly peak demand, and per capita income were considered for forecasting the electricity consumption of a state in a developing country. Initially, a multiple linear regression model has been developed. Then, the coefficients of the regression model were optimized using two heuristic approaches namely genetic algorithm and simulated annealing. The mean absolute percentage error obtained for the three models were 2.00 for multiple linear regression model, 1.94 for genetic algorithm based linear regression and 1.86 for simulated annealing based linear regression.

scholarly journals Factors Affecting the Productivity of the Big Onion in Hambantota District during the Off Season

2019 ◽  
pp. 1-10
Author(s):  
M. K. M. Sulochana ◽  
L. S. Nawarathna
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Factors Affecting ◽  
Absolute Percentage Error ◽  
Average Productivity

Aim: The main aim of this study is to identify the factors affecting the big onion productivity of Hambantota district during the off-season. Moreover, we identify the average productivity per acre from Hambantota district and compare it with the other areas that cultivated the big onion. Further, identify the main issues encountered in big onion cultivation in Hambantota and identify the critical contributing factors for the big onion cultivation in this area. Place and Duration of Study: During the off seasons in 2015 to 2016 in Hambantota District. Methodology: Sample data was collected from 201 farmers in Hambantota district. Multiple linear regression model was used to identify the factors affecting the big onion productivity in Hambantota district during the off-season. The normality assumption of the regression model was checked using Kolmogorov–Smirnov test, Shapiro Wilk normality test and Skewness and Kurtosis test. Pearson, Spearman’s Rank and Partial correlation tests were used to check the correlations between variables. Mean absolute percentage error (MAPE) and Symmetrical Mean absolute percentage error (SMAPE) values were used to validate the fitted model. Results: By the multiple linear regression model main factors affecting the productivity of big onion in Hambantota area were Seasonal Months, Monthly Income, Subsidies Fertilizer and Cultivated Quantity. And the R-squared value was most like to 80% and this means these independent variables were described 80% of the dependent variable.  Model accuracies were reported as 98.48% and 98.49% from MAPE and SMAPE respectively. Therefore, this multiple linear regression model was suitable for this study. Further, the model determined the affected factors for the big onion cultivation in Hambantota district during the off-season. Conclusion: Hambantota district average productivity was less than other areas. Big onion productivity of Matale is more than 2 times greater than big onion productivity of Hambantota. Off season big onion cultivation in Hambantota district is not very effective because of the average productivity is less than other areas in Sri Lanka.

scholarly journals Enterprise Power Consumption Data and GDP Forecasting Based on Ensemble Algorithms

2021 ◽  
Vol 233 ◽  
pp. 01030
Author(s):  
Yibin Xu ◽  
Lu He ◽  
Ying Liang ◽  
Jianhong Si ◽  
Yonglong Bao
Keyword(s):  
Linear Regression ◽  
Power Consumption ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Independent Variables ◽  
Consumption Data ◽  
Dependent Variables ◽  
Ensemble Algorithms

This paper focuses on the development of regional GDP and proposes a method proposed for forecast of enterprise power consumption data and GDP based on ensemble algorithms. The enterprise power consumption data are used as independent variables and GDP data as dependent variables. A multiple linear regression model is selected as the primary learner for training and its outputs will be sorted into a new dataset of input features to train a secondary learner. The forecast of GDP is thus realized through ensemble learning.

scholarly journals The Use of Multiple Linear Regression Model for Predicting The Influence of Weather to The Prevalence of Newcastle Disease in Chicken in North Aceh

2008 ◽  
Vol 2 (1) ◽  
Author(s):  
Mahdi Abrar
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Newcastle Disease ◽  
Linear Regression Model ◽  
Minimum Temperature ◽  
Multiple Linear Regression Model ◽  
Maximum Temperature ◽  
Independent Variables ◽  
Dependent Variables

The objective of this research is to see the influence of weather on the prevalence of Newcastle Disease (ND) in chicken in Kabupaten Aceh Utara (North Aceh). Data used in this research were obtained from Dinas Peternakan North Aceh for the number of chicken suffered ND and from Badan Meteorologi dan Geofisika Lhokseumawe, North Aceh for the form of weather. Multiple Linear Regression Model with five independent variables (the average of rainfall per month, the average of maximum temperature, the average of minimum temperature, the velocity of the wind, and the average of humidity per month) was used to see the influence of wheather to the prevalence of Newcastle Disease. Proportion the number of chicken suffered from ND which is the ratio of the number of chicken suffered from ND to the total number of chicken was used as dependent variables. The result shows that the best model is Ŷ= 120.529278 – 1.33 x wind humidity + 1.907 x wind velocity.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

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