Application Of Linear Regression Model Research Articles

Estimating optimal split delivery vehicle routing problem solution values

This paper explores the application of linear regression models to estimate the optimal solution value (i.e., the sum of tour lengths) for the Split Delivery Vehicle Routing Problem (SDVRP). We present novel models that integrate topological features along with the mean and standard deviation of feasible solution values, achieving an impressive accuracy with an error margin of approximately 3%. To obtain random feasible solutions for the SDVRP quickly, we propose a modified Clarke & Wright algorithm with split delivery (MCWSD). Our results demonstrate the potential of extending our earlier work to more complex routing problems, highlighting the importance of incorporating diverse features to obtain accurate approximations.

Application of Linear Regression Models in Substantive Analytical Procedures for Auditing Asset Management

Application of Linear Regression Models in Substantive Analytical Procedures for Auditing Asset Management

Application of linear regression model in the design of furniture products for the aged

Abstract As an important part of product design, furniture is one of the most important indoor products. It is also a product that is used more frequently in daily life and closely related to human beings. Therefore, the investigation of furniture product design for the aged is put forward. In this article, the physical and mechanical properties of the reconstituted wood material and the strength properties of the furniture for the aged were tested and analyzed. The results of dynamic mechanical properties of materials tested by three FFT nondestructive testing methods and static mechanical properties tested by three-point bending failure test method were compared. The relationship between different elastic moduli and static bending strength was analyzed by linear regression, and the prediction model of static bending strength of reconstituted wood based on different elastic moduli was proposed. The furniture design of reconstituted wood of Wattle was carried out. The finite element method was used to calculate and analyze the structural strength of wattle reconstituted wood furniture under static load. The results showed that the structure design of wattle reconstituted wood furniture was reasonable and had enough structural strength to be used safely for the elderly furniture.

Application of Linear Regression Model of Gpnmb Gene in Rat Injury Time Estimation.

To investigate the effects of injury time, postmortem interval (PMI) and postmortem storage temperature on mRNA expression of glycoprotein non-metastatic melanoma protein B (Gpnmb), and to establish a linear regression model between Gpnmb mRNA expression and injury time, to provide aimed at providing potential indexes for injury time estimation. Test group SD rats were anesthetized and subjected to blunt contusion and randomly divided into 0 h, 4 h, 8 h, 12 h, 16 h, 20 h and 24 h groups after injury, with 18 rats in each group. After cervical dislocation, 6 rats in each group were collected and stored at 0 ℃, 16 ℃ and 26 ℃, respectively. The muscle tissue samples of quadriceps femoris injury were collected at 0 h, 12 h and 24 h postmortem at the same temperature. The grouping method and treatment method of the rats in the validation group were the same as above. The expression of Gpnmb mRNA in rat skeletal muscle was detected by RT-qPCR. The Pearson correlation coefficient was used to evaluate the correlation between Gpnmb mRNA expression and injury time, PMI, and postmortem storage temperature. SPSS 25.0 software was used to construct a linear regression model, and the validation group data was used for the back-substitution test. The expression of Gpnmb mRNA continued to increase with the prolongation of injury time, and the expression level was highly correlated with injury time (P<0.05), but had little correlation with PMI and postmortem storage temperature (P>0.05). The linear regression equation between injury time (y) and Gpnmb mRNA relative expression (x) was y=0.611 x+4.489. The back-substitution test proved that the prediction of the model was accurate. The expression of Gpnmb mRNA is almost not affected by the PMI and postmortem storage temperature, but is mainly related to the time of injury. Therefore, a linear regression model can be established to infer the time of injury.

Uticaj berzanskog prometa na BDP u Republici Srbiji

Stock exchange turnover growth increases the capital performance of a country. This paper examines the stock exchange turnover trends after the global financial crisis while considering the novel COVID-19 global crisis in the Republic of Serbia. Moreover, this paper estimates the correlation between the stock exchange turnover and GDP indicators. Since the COVID-19 crises is recent, the available official data for the last two years are not complete yet. Therefore, the aim of this research is to measure the impact of stock exchange turnover on economic growth in the Republic of Serbia for the period 2006-2020, with respective predictions. The methodological approach consists of the application of linear regression model, with previous statistical adequacy tests. The final results of this research indicate that the growth of stock exchange turnover has positive and statistically significant impact on GDP in this country. However, the prediction analysis indicates that the stock exchange turnover may have a negative impact on GDP in the next years, which is why there are several suggestions for decision makers and further research.

Short-term wind speed forecasting over complex terrain using linear regression models and multivariable LSTM and NARX networks in the Andes Mountains, Ecuador

Wind speed forecasting systems over complex terrain at high altitude are very complex and conventional forecasting systems are unable to be applied due to wind variability. This study proposes an approach developed specifically for this study with application of linear regression models as baseline, and Recurrent Neural Networks (RNN): Long Short Term Memory (LSTM) network, and Dynamic Neural Networks (DNN): Nonlinear Autoregressive Exogenous (NARX) network to perform accurate wind speed forecasting in complex terrain in the Ecuadorian Andes to identify feasible places for wind energy applications. This work starts with the installation of two meteorological stations within of the mountainous zone of study to collect measured variables during 2018. Later on, the measured variables were evaluated by using statistical tools and Pearson Correlation Coefficient (PCC) to determine the input variables. Finally, the proposed forecasting models were trained, validated, and tested by using measured data. The DNN and RNN models were compared to determine the best performance through the statistical error forecast measurements as follows, Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and correlation coefficient. The comparison results of the proposed models indicated that the most precise values were for multivariable LSTM network, suggesting this model as a powerful approach to forecast wind speed over complex terrain, demonstrating the importance by using measured variables. Furthermore, the forecasted wind speed showed high values which are suitable for high wind power generation.

Church Population Growth Prediction Using Predictive Analytic (Linear Regression Model) Technique

The world today has become highly technological and Information and Communication Technology (ICT) has been identified as one of the basic pillars on which the modern society stands. In Nigeria today, competency in basic ICT skills is now regarded as one of the basic requirements for our day-day activities in religion, economic, financial and political. The use of available ICT tools equally enhances the validity of results and assist management in planning and budgeting for growth. The church cannot be an exception in this regard as “spreading the gospel” is the goal of the churches by growing the number of new members using available tools. The issue of church growth transcends many areas of church activities and the application of the ICT infrastructures could be gainfully put into use in some of these areas for greater impact on church development. The difficulties confronting church organisations in respect of church growth are becoming unsurmountable for those that have refused to embrace and deploy Information and Communication Technologies to enhance growth and its resultant complexities. In this paper, a system capable of predicting church growth with the application of Linear Regression Model (LRM) using the historic data of a growing church is presented. The results demonstrated the importance of predicting the population of organisations such as religious centers that often have a growing teeming population of members. Such results of prediction using ICT tools would be an essential guide in the budgeting requirements of such organisations. Keywords: Prediction, growth, tools, ICT, normalize, linear, budgeting DOI: 10.7176/CEIS/12-1-05 Publication date: January 31 st 2021

The Forecast Research of Linear Regression Forecast Model in National Economy

Based on application of Linear Regression Model in national economy. Statistical Yearbook of China is a statistical data book published by China. Some different types of data in China have been documented every year. The dissertation is researching some part of national economy in Statistical Yearbook of China to research the relationship between GNP (gross national product) and various data, and afterwards to establish model to achieve some rationalization suggestions for the future of the National Economy. The basic method for data mining is prediction. The method proposed in this dissertation will use multiple linear regression model combined with Gray Prediction to research to achieve processing information. This dissertation uses forward stepwise regression or backward stepwise regression in multiple linear regression to make specific data model. Afterwards some fuzzy data will be analysed with Gray prediction model. Finally, the combination of the two realizes the data prediction algorithm. The main calculation tools that will be used in this dissertation are SPSS and MATLAB. The prediction results are based on model which is obtained in this dissertation. After comparing the multiple linear regression and the grey prediction model, the researcher found the algorithm of this dissertation is more accurate, thereby verifying the rationality of the prediction model in this dissertation.

Istraživanje determinanti problematičnih kredita bankarskog sektora Srbije

The aim of this paper is to identify and analyze important internal and macroeconomic factors that determine the level of non-performing loans in the domestic banking sector thus contributing to the process of managing banks' credit risk. The research is based on the application of linear regression model (OLS). In accordance with the results, we have concluded that the growth in lending activities and the concentration of the five largest banks contributed to the increase in the share of non-performing loans in the credit portfolio of the banking sector.

Aero-Material Consumption Prediction Based on Linear Regression Model

It is indispensable to scientifically predict the consumption of aero-material spare parts and to make scientific decisions on aviation equipment maintenance resources and make full use of existing resources to improve maintenance capability. In this paper, the mathematic model and calculation method of linear regression model are introduced. And the parameter estimation and model test method of linear regression model is discussed. A linearization method is proposed for nonlinear problems. The application of linear regression model in forecasting the consumption of aero-material spare parts is analyzed by examples. Finally, the regression equation is analyzed for significance analysis, variance analysis and residual analysis. According to the analysis results, the regression equation is modified as necessary to further improve the prediction accuracy. The results show that the linear regression model is feasible and effective for the prediction of aero-material spare parts consumption.

Commentary on the statistical properties of noise and its implication on general linear models in functional near-infrared spectroscopy.

Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique that uses low levels of light to measure changes in cerebral blood oxygenation levels. In the majority of NIRS functional brain studies, analysis of this data is based on a statistical comparison of hemodynamic levels between a baseline and task or between multiple task conditions by means of a linear regression model: the so-called general linear model. Although these methods are similar to their implementation in other fields, particularly for functional magnetic resonance imaging, the specific application of these methods in fNIRS research differs in several key ways related to the sources of noise and artifacts unique to fNIRS. In this brief communication, we discuss the application of linear regression models in fNIRS and the modifications needed to generalize these models in order to deal with structured (colored) noise due to systemic physiology and noise heteroscedasticity due to motion artifacts. The objective of this work is to present an overview of these noise properties in the context of the linear model as it applies to fNIRS data. This work is aimed at explaining these mathematical issues to the general fNIRS experimental researcher but is not intended to be a complete mathematical treatment of these concepts.

Biases in greenhouse gases static chambers measurements in stabilization ponds: Comparison of flux estimation using linear and non-linear models

The closed static chamber technique is widely used to quantify greenhouse gases (GHG) i.e. CH4, CO2 and N2O from aquatic and wastewater treatment systems. However, chamber-measured fluxes over air–water interfaces appear to be subject to considerable uncertainty, depending on the chamber design, lack of air mixing in the chamber, concentration gradient changes during the deployment, and irregular eruptions of gas accumulated in the sediment. In this study, the closed static chamber technique was tested in an anaerobic pond operating under tropical conditions. The closed static chambers were found to be reliable to measure GHG, but an intrinsic limitation of using closed static chambers is that not all the data for gas concentrations measured within a chamber headspace can be used to estimate the flux due to gradient concentration curves with non-plausible and physical explanations. Based on the total data set, the percentage of curves accepted was 93.6, 87.2, and 73% for CH4, CO2 and N2O, respectively. The statistical analyses demonstrated that only considering linear regression was inappropriate (i.e. approximately 40% of the data for CH4, CO2 and N2O were best fitted to a non-linear regression) for the determination of GHG flux from stabilization ponds by the closed static chamber technique. In this work, it is clear that when R2adj-non-lin > R2adj-lin, the application of linear regression models is not recommended, as it leads to an underestimation of GHG fluxes by 10–50%. This suggests that adopting only or mostly linear regression models will affect the GHG inventories obtained by using closed static chambers. According to our results, the misuse of the usual R2 parameter and only the linear regression model to estimate the fluxes will lead to reporting erroneous information on the real contribution of GHG emissions from wastewater. Therefore, the R2adj and non-linear regression model analysis should be used to reduce the biases in flux estimation by the inappropriate application of only linear regression models.

A Research on Tourism Market Forecast and Evaluation Based on the Application of Linear Regression Model

Tourism market forecast is an important tool to analyze the tourism market trends, to understand the trends of the needs of tourism market, as well as to forecast tourism demand in the direction of regional tourism development, by which the great significance for tourism product development strategy and market strategy could be carried on. The traditional prediction methods paid much more attention to the accuracy of the forecasts, ignoring the results of the feasibility of forecasting and predicting operability, which had made it difficult to predict the results of scientific testing. This paper created a scientific evaluation system for predictive value, both to ensure the accuracy, stability of the predicted value, and to ensure the feasibility of forecasting and predicting the results of operation based on simple linear regression applied to the forecasts of the tourism market. Therefore, the outcome of the tourism market forecasts in this paper can provide a scientific basis for the government to develop the policy for tourism industry.

Application of linear regression model on chatter threshold delineation

This research is to parameterize the variation in reconstructed phase space attractor Poincaré sections during the milling process in order to identify the transition of system from stable to unstable condition. Vibration is continuously acquired during the milling process and converted to the form of reconstructed time series. A regression model is developed to compute the trend of changes in the reconstructed phase space attractor Poincaré sections and output a numerical value indicating the level of stability. A threshold value of the trend value defines the boundary of stable and unstable states of milling process. The method monitors the state of system without the necessity to have analytical relationship between cutting parameters and milling dynamics. It can also be used as an online monitoring process to detect the onset of chatter.

Modeling of groundwater recharge using a multiple linear regression (MLR) recharge model developed from geophysical parameters: a case of groundwater resources management

In this paper we developed a simple multiple linear regression (MLR) recharge model that relates the recharge estimates obtained from rainfall to the geophysical parameters obtained from the interpretation of two-dimensional (2D) resistivity imaging data for the purpose of efficient groundwater resources management in the southern part of Perak, Malaysia through recharge rate estimation and prediction. Through application of linear regression model, the estimated recharge from rainfall and the corresponding estimated unsaturated layer resistivity and its thickness (Depth to aquifer top) parameters obtained from geophysical measurements were regressed in R software written code environment for generating a MLR recharge model. The sensitivity of analyzed results of the MLR recharge model based on the parameter estimation of the model predictors (resistivity and depth) evaluated at Pr ≤ 0.05 is 5.39 × 10−06 and 8.39 × 10−04, respectively. The accuracy and predictive power test conducted on the developed model using both t test and $$ \chi^{2} $$ distribution at α = 5 % significance level established the model estimation and prediction capability. The obtained results of $$ \chi^{2} $$ distribution test and parameters estimation test confirmed the reliability and accuracy of the proposed model in recharge rate estimation and prediction in the area. The application of the MLR recharge model gives estimate of 242.30 mm/year for regional groundwater recharge rate in the area. Through GIS tool, the MLR recharge model was used to produce groundwater recharge rate prediction map. A quick and independent estimate of recharge by simple geophysical measurement has been established based on these results. The information on the prediction map could serve as a scientific basis for groundwater resources management and exploration in the area. The approach suggests a new application of geoelectric parameters in determining recharge rate due to infiltration. The technique provides a good alternative to other methods used for this purpose.

Applicability of Linear Regression Model for the Estimation of Mobility Factor of Heavy Metal in Contaminated Soil against Native Concentrations

This study posits a far-reaching aggregation of considerations for the immediate natural conditions of soil under assessment with the application of simplistic linear regression analysis to deduce a mobility factor for heavy metals in soil. Attempting thereby to eliminate the weaknesses observed in adsorption tests, the rigors of infiltration tests and chemical fractionation procedures. Therefore vertical depth profile (0-5cm, 5-50cm and 50-100cm) of contaminated soil samples at municipal dumpsites and native soil (uncontaminated reference samples) concentrations of Cu, Mn, Pb and Zn were analysed using inductively coupled plasma - optical emission spectrometry (ICP-OES). The results of linear regression analysis considered the value of slope in the equation for the estimation of mobility factor of heavy metals in contaminated soil. The following average order of mobility factor for the heavy metals, Mn (37.36) > Pb (0.48) > Cu (0.38) > Zn (0.19) was estab- lished. This order was certainly not dependent on concentration, which allude to already established facts that several factors influence the mobility of heavy metals in soil. Apart from the estimation of mobility factors for the heavy metals, the equation was also found to be useful in predicting the concentration of heavy metals at dumpsites given a reference concentration value.

One Method of Image Processing and its Numerical Analysis

The paper discusses application of linear regression models to image transformation and pattern recognition, oriented towards creation of technology for fast processing of digital images. Theoretical and heuristic expectations are confronted with results of numerical analysis. Digital images are considered to be sets of samples taken from the surface of brightness levels. Adequate regression models, i.e. analytic expressions of functions for representation of these surfaces are sought. Images are represented by vectors of coefficients of the model calculated using the least squares method. Classification of images further is based on classification results of coefficient vectors of linear regression models.

Nationwide Freight Generation Models: A Spatial Regression Approach

This paper investigates the application of linear regression models and modeling techniques in predicting freight generation at the national level within the U.S. Specifically, the paper seeks to improve the performance and fit of linear regression models of freight generation. We provide insight into different variable transformation techniques, evaluate the use of spatial regression variables, and apply a spatial regression modeling methodology to correct for spatial autocorrelation. We conclude that the spatial regression model is the preferred specification for freight generation at the national level. The proliferation of Geographic Information Systems (GIS) within planning agencies affords more widespread use of spatial regression and our results indicate this technique would provide improvement to models that have been traditionally limited by insufficient data.

Forecasting the New York stock exchange composite index with past price and interest rate on condition of volume spike

Abstract We identify trading volume spikes through use of the template matching technique from statistical pattern recognition. For those trading days meeting the condition signifying volume spike recognition, application of linear regression models the future change in price using historical price and prime interest rate values. Also, we train a nonlinear neural network model and use it as a basis for simulated trading, which includes consideration of transaction costs and cash dividends. We illustrate and test with New York Stock Exchange Composite Index data for the period from 1981 to 1999. Results are positive, robust, systematic, economically significant, and informative as to the role of trading volume in the stock market mechanism.

Nonlinear dynamics of milling processes

In this article, dynamics and stability of milling operations with cylindrical end mills are investigated. A unified–mechanics–based model, which allows for both regenerative effects and loss–of–contact effects, is presented for study of partial–immersion, high–immersion and slotting operations. Reduced–order models that can be used for certain milling operations such as full–immersion operations and finishing cuts are also presented. On the basis of these models, the loss of stability of periodic motions of the workpiece–tool system is assessed by using Poincare sections and the numerical predictions of stable and unstable motions are found to correlate well with the corresponding experimental observations. Bifurcations experienced by periodic motions of the workpiece–tool system with respect to quasi–static variation of parameters such as axial depth of cut are examined and discussed. For partial–immersion operations, consideration of both time–delay effects and loss–of–contact effects is shown to have a significant influence on the structure of the stability boundaries in the space of spindle speed and axial depth of cut. The sensitivity of system dynamics to multiple–regenerative effects, mode–coupling effects and feed rate is also discussed.