**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**3583

# Search results for: multiple linear regression

##### 3583 Research on the Problems of Housing Prices in Qingdao from a Macro Perspective

**Authors:**
Liu Zhiyuan,
Sun Zongdi,
Liu Zhiyuan,
Sun Zongdi

**Abstract:**

Qingdao is a seaside city. Taking into account the characteristics of Qingdao, this article established a multiple linear regression model to analyze the impact of macroeconomic factors on housing prices. We used stepwise regression method to make multiple linear regression analysis, and made statistical analysis of F test values and T test values. According to the analysis results, the model is continuously optimized. Finally, this article obtained the multiple linear regression equation and the influencing factors, and the reliability of the model was verified by F test and T test.

**Keywords:**
Housing prices,
multiple linear regression model,
macroeconomic factors,
Qingdao City.

##### 3582 A Comparison of the Sum of Squares in Linear and Partial Linear Regression Models

**Authors:**
Dursun Aydın

**Abstract:**

**Keywords:**
Partial Linear Regression Model,
Linear RegressionModel,
Residuals,
Deviance,
Smoothing Spline.

##### 3581 Internet Purchases in European Union Countries: Multiple Linear Regression Approach

**Authors:**
Ksenija Dumičić,
Anita Čeh Časni,
Irena Palić

**Abstract:**

This paper examines economic and Information and Communication Technology (ICT) development influence on recently increasing Internet purchases by individuals for European Union member states. After a growing trend for Internet purchases in EU27 was noticed, all possible regression analysis was applied using nine independent variables in 2011. Finally, two linear regression models were studied in detail. Conducted simple linear regression analysis confirmed the research hypothesis that the Internet purchases in analyzed EU countries is positively correlated with statistically significant variable Gross Domestic Product *per capita *(GDPpc). Also, analyzed multiple linear regression model with four regressors, showing ICT development level, indicates that ICT development is crucial for explaining the Internet purchases by individuals, confirming the research hypothesis.

**Keywords:**
European Union,
Internet purchases,
multiple linear regression model,
outlier

##### 3580 Multi-Linear Regression Based Prediction of Mass Transfer by Multiple Plunging Jets

**Abstract:**

The paper aims to compare the performance of vertical and inclined multiple plunging jets and to model and predict their mass transfer capacity by multi-linear regression based approach. The multiple vertical plunging jets have jet impact angle of θ = 90^{O}; whereas, multiple inclined plunging jets have jet impact angle of θ = 60^{O}. The results of the study suggests that mass transfer is higher for multiple jets, and inclined multiple plunging jets have up to 1.6 times higher mass transfer than vertical multiple plunging jets under similar conditions. The derived relationship, based on multi-linear regression approach, has successfully predicted the volumetric mass transfer coefficient (K_{L}a) from operational parameters of multiple plunging jets with a correlation coefficient of 0.973, root mean square error of 0.002 and coefficient of determination of 0.946. The results suggests that predicted overall mass transfer coefficient is in good agreement with actual experimental values; thereby, suggesting the utility of derived relationship based on multi-linear regression based approach and can be successfully employed in modeling mass transfer by multiple plunging jets.

**Keywords:**
Mass transfer,
multiple plunging jets,
multi-linear regression.

##### 3579 Relationship between Sums of Squares in Linear Regression and Semi-parametric Regression

**Authors:**
Dursun Aydın,
Bilgin Senel

**Abstract:**

**Keywords:**
Semi-parametric regression,
Penalized LeastSquares,
Residuals,
Deviance,
Smoothing Spline.

##### 3578 The Relative Efficiency of Parameter Estimation in Linear Weighted Regression

**Authors:**
Baoguang Tian,
Nan Chen

**Abstract:**

A new relative efficiency in linear model in reference is instructed into the linear weighted regression, and its upper and lower bound are proposed. In the linear weighted regression model, for the best linear unbiased estimation of mean matrix respect to the least-squares estimation, two new relative efficiencies are given, and their upper and lower bounds are also studied.

**Keywords:**
Linear weighted regression,
Relative efficiency,
Mean matrix,
Trace.

##### 3577 Clustering Protein Sequences with Tailored General Regression Model Technique

**Authors:**
G. Lavanya Devi,
Allam Appa Rao,
A. Damodaram,
GR Sridhar,
G. Jaya Suma

**Abstract:**

**Keywords:**
Clustering,
General Regression Model,
Protein
Sequences,
Similarity Measure.

##### 3576 Comparison of Polynomial and Radial Basis Kernel Functions based SVR and MLR in Modeling Mass Transfer by Vertical and Inclined Multiple Plunging Jets

**Abstract:**

**Keywords:**
Mass transfer,
multiple plunging jets,
polynomial
and radial basis kernel functions,
Support Vector Regression.

##### 3575 On the outlier Detection in Nonlinear Regression

**Authors:**
Hossein Riazoshams,
Midi Habshah,
Jr.,
Mohamad Bakri Adam

**Abstract:**

**Keywords:**
Nonlinear Regression,
outliers,
Gradient,
LeastSquare,
M-estimate,
MM-estimate.

##### 3574 Economic Dispatch Fuzzy Linear Regression and Optimization

**Authors:**
A. K. Al-Othman

**Abstract:**

**Keywords:**
Economic Dispatch,
Fuzzy Linear Regression (FLP)and Optimization.

##### 3573 Climate Change in Albania and Its Effect on Cereal Yield

**Abstract:**

This study is focused on analyzing climate change in Albania and its potential effects on cereal yields. Initially, monthly temperature and rainfalls in Albania were studied for the period 1960-2021. Climacteric variables are important variables when trying to model cereal yield behavior, especially when significant changes in weather conditions are observed. For this purpose, in the second part of the study, linear and nonlinear models explaining cereal yield are constructed for the same period, 1960-2021. The multiple linear regression analysis and lasso regression method are applied to the data between cereal yield and each independent variable: average temperature, average rainfall, fertilizer consumption, arable land, land under cereal production, and nitrous oxide emissions. In our regression model, heteroscedasticity is not observed, data follow a normal distribution, and there is a low correlation between factors, so we do not have the problem of multicollinearity. Machine learning methods, such as Random Forest (RF), are used to predict cereal yield responses to climacteric and other variables. RF showed high accuracy compared to the other statistical models in the prediction of cereal yield. We found that changes in average temperature negatively affect cereal yield. The coefficients of fertilizer consumption, arable land, and land under cereal production are positively affecting production. Our results show that the RF method is an effective and versatile machine-learning method for cereal yield prediction compared to the other two methods: multiple linear regression and lasso regression method.

**Keywords:**
Cereal yield,
climate change,
machine learning,
multiple regression model,
random forest.

##### 3572 A Multiple Linear Regression Model to Predict the Price of Cement in Nigeria

**Authors:**
Kenneth M. Oba

**Abstract:**

This study investigated factors affecting the price of cement in Nigeria, and developed a mathematical model that can predict future cement prices. Cement is key in the Nigerian construction industry. The changes in price caused by certain factors could affect economic and infrastructural development; hence there is need for proper proactive planning. Secondary data were collected from published information on cement between 2014 and 2019. In addition, questionnaires were sent to some domestic cement retailers in Port Harcourt in Nigeria, to obtain the actual prices of cement between the same periods. The study revealed that the most critical factors affecting the price of cement in Nigeria are inflation rate, population growth rate, and Gross Domestic Product (GDP) growth rate. With the use of data from United Nations, International Monetary Fund, and Central Bank of Nigeria databases, amongst others, a Multiple Linear Regression model was formulated. The model was used to predict the price of cement for 2020-2025. The model was then tested with 95% confidence level, using a two-tailed t-test and an F-test, resulting in an R^{2} of 0.8428 and R^{2} (adj.) of 0.6069. The results of the tests and the correlation factors confirm the model to be fit and adequate. This study will equip researchers and stakeholders in the construction industry with information for planning, monitoring, and management of present and future construction projects that involve the use of cement.

**Keywords:**
Cement price,
multiple linear regression model,
Nigerian Construction Industry,
price prediction.

##### 3571 Studding of Number of Dataset on Precision of Estimated Saturated Hydraulic Conductivity

**Authors:**
M. Siosemarde,
M. Byzedi

**Abstract:**

**Keywords:**
dataset,
precision,
saturated hydraulic conductivity,
soil and statistics.

##### 3570 A Fuzzy Linear Regression Model Based on Dissemblance Index

**Authors:**
Shih-Pin Chen,
Shih-Syuan You

**Abstract:**

**Keywords:**
Dissemblance index,
fuzzy linear regression,
graded
mean integration,
mathematical programming.

##### 3569 Two New Relative Efficiencies of Linear Weighted Regression

**Authors:**
Shuimiao Wan,
Chao Yuan,
Baoguang Tian

**Abstract:**

**Keywords:**
Linear weighted regression,
Relative efficiency,
Lower bound,
Parameter estimation.

##### 3568 Fuzzy Logic Approach to Robust Regression Models of Uncertain Medical Categories

**Authors:**
Arkady Bolotin

**Abstract:**

Dichotomization of the outcome by a single cut-off point is an important part of various medical studies. Usually the relationship between the resulted dichotomized dependent variable and explanatory variables is analyzed with linear regression, probit regression or logistic regression. However, in many real-life situations, a certain cut-off point dividing the outcome into two groups is unknown and can be specified only approximately, i.e. surrounded by some (small) uncertainty. It means that in order to have any practical meaning the regression model must be robust to this uncertainty. In this paper, we show that neither the beta in the linear regression model, nor its significance level is robust to the small variations in the dichotomization cut-off point. As an alternative robust approach to the problem of uncertain medical categories, we propose to use the linear regression model with the fuzzy membership function as a dependent variable. This fuzzy membership function denotes to what degree the value of the underlying (continuous) outcome falls below or above the dichotomization cut-off point. In the paper, we demonstrate that the linear regression model of the fuzzy dependent variable can be insensitive against the uncertainty in the cut-off point location. In the paper we present the modeling results from the real study of low hemoglobin levels in infants. We systematically test the robustness of the binomial regression model and the linear regression model with the fuzzy dependent variable by changing the boundary for the category Anemia and show that the behavior of the latter model persists over a quite wide interval.

**Keywords:**
Categorization,
Uncertain medical categories,
Binomial regression model,
Fuzzy dependent variable,
Robustness.

##### 3567 Orthogonal Regression for Nonparametric Estimation of Errors-in-Variables Models

**Authors:**
Anastasiia Yu. Timofeeva

**Abstract:**

Two new algorithms for nonparametric estimation of errors-in-variables models are proposed. The first algorithm is based on penalized regression spline. The spline is represented as a piecewise-linear function and for each linear portion orthogonal regression is estimated. This algorithm is iterative. The second algorithm involves locally weighted regression estimation. When the independent variable is measured with error such estimation is a complex nonlinear optimization problem. The simulation results have shown the advantage of the second algorithm under the assumption that true smoothing parameters values are known. Nevertheless the use of some indexes of fit to smoothing parameters selection gives the similar results and has an oversmoothing effect.

**Keywords:**
Grade point average,
orthogonal regression,
penalized regression spline,
locally weighted regression.

##### 3566 Density Estimation using Generalized Linear Model and a Linear Combination of Gaussians

**Authors:**
Aly Farag,
Ayman El-Baz,
Refaat Mohamed

**Abstract:**

In this paper we present a novel approach for density estimation. The proposed approach is based on using the logistic regression model to get initial density estimation for the given empirical density. The empirical data does not exactly follow the logistic regression model, so, there will be a deviation between the empirical density and the density estimated using logistic regression model. This deviation may be positive and/or negative. In this paper we use a linear combination of Gaussian (LCG) with positive and negative components as a model for this deviation. Also, we will use the expectation maximization (EM) algorithm to estimate the parameters of LCG. Experiments on real images demonstrate the accuracy of our approach.

**Keywords:**
Logistic regression model,
Expectationmaximization,
Segmentation.

##### 3565 Analyzing of Public Transport Trip Generation in Developing Countries; A Case Study in Yogyakarta, Indonesia

**Authors:**
S. Priyanto,
E.P Friandi

**Abstract:**

Yogyakarta, as the capital city of Yogyakarta Province, has important roles in various sectors that require good provision of public transportation system. Ideally, a good transportation system should be able to accommodate the amount of travel demand. This research attempts to develop a trip generation model to predict the number of public transport passenger in Yogyakarta city. The model is built by using multiple linear regression analysis, which establishes relationship between trip number and socioeconomic attributes. The data consist of primary and secondary data. Primary data was collected by conducting household surveys which randomly selected. The resulted model is further applied to evaluate the existing TransJogja, a new Bus Rapid Transit system serves Yogyakarta and surrounding cities, shelters.

**Keywords:**
Multiple linear regression,
shelter evaluation,
travel demand,
trip generation.

##### 3564 Delay-independent Stabilization of Linear Systems with Multiple Time-delays

**Authors:**
Ping He,
Heng-You Lan,
Gong-Quan Tan

**Abstract:**

**Keywords:**
Linear system,
Delay-independent stabilization,
Lyapunovfunctional,
Riccati algebra matrix equation.

##### 3563 A Cost Optimization Model for the Construction of Bored Piles

**Authors:**
Kenneth M. Oba

**Abstract:**

Adequate management, control, and optimization of cost is an essential element for a successful construction project. A multiple linear regression optimization model was formulated to address the problem of costs associated with pile construction operations. A total of 32 PVC-reinforced concrete piles with diameter of 300 mm, 5.4 m long, were studied during the construction. The soil upon which the piles were installed was mostly silty sand, and completely submerged in water at Bonny, Nigeria. The piles are friction piles installed by boring method, using a piling auger. The volumes of soil removed, the weight of reinforcement cage installed, and volumes of fresh concrete poured into the PVC void were determined. The cost of constructing each pile based on the calculated quantities was determined. A model was derived and subjected to statistical tests using Statistical Package for the Social Sciences (SPSS) software. The model turned out to be adequate, fit, and have a high predictive accuracy with an R2 value of 0.833.

**Keywords:**
Cost optimization modelling,
multiple linear models,
pile construction,
regression models.

##### 3562 Computational Aspects of Regression Analysis of Interval Data

**Authors:**
Michal Cerny

**Abstract:**

We consider linear regression models where both input data (the values of independent variables) and output data (the observations of the dependent variable) are interval-censored. We introduce a possibilistic generalization of the least squares estimator, so called OLS-set for the interval model. This set captures the impact of the loss of information on the OLS estimator caused by interval censoring and provides a tool for quantification of this effect. We study complexity-theoretic properties of the OLS-set. We also deal with restricted versions of the general interval linear regression model, in particular the crisp input – interval output model. We give an argument that natural descriptions of the OLS-set in the crisp input – interval output cannot be computed in polynomial time. Then we derive easily computable approximations for the OLS-set which can be used instead of the exact description. We illustrate the approach by an example.

**Keywords:**
Linear regression,
interval-censored data,
computational complexity.

##### 3561 Empirical Statistical Modeling of Rainfall Prediction over Myanmar

**Authors:**
Wint Thida Zaw,
Thinn Thu Naing

**Abstract:**

**Keywords:**
Polynomial Regression,
Rainfall Forecasting,
Statistical forecasting.

##### 3560 Quantitative Structure Activity Relationship and Insilco Docking of Substituted 1,3,4-Oxadiazole Derivatives as Potential Glucosamine-6-Phosphate Synthase Inhibitors

**Authors:**
Suman Bala,
Sunil Kamboj,
Vipin Saini

**Abstract:**

*Candida albicans*and

*Aspergillus niger*using computer assisted multiple regression analysis. The study has shown the better relationship between antifungal activities with respect to various descriptors established by multiple regression analysis. The analysis has shown statistically significant correlation with R

^{2}values 0.932 and 0.782 against

*Candida albicans*and

*Aspergillus niger*respectively. These derivatives were further subjected to molecular docking studies to investigate the interactions between the target compounds and amino acid residues present in the active site of glucosamine-6-phosphate synthase. All the synthesized compounds have better docking score as compared to standard fluconazole. Our results could be used for the further design as well as development of optimal and potential antifungal agents.

**Keywords:**
1,
3,
4-Oxadiazole,
QSAR,
Multiple linear regression,
Docking,
Glucosamine-6-Phosphate Synthase.

##### 3559 Artificial Neural Network based Modeling of Evaporation Losses in Reservoirs

**Authors:**
Surinder Deswal,
Mahesh Pal

**Abstract:**

**Keywords:**
Artificial neural network,
evaporation losses,
multiple linear regression,
modeling.

##### 3558 A Hybrid Model of ARIMA and Multiple Polynomial Regression for Uncertainties Modeling of a Serial Production Line

**Authors:**
Amir Azizi,
Amir Yazid b. Ali,
Loh Wei Ping,
Mohsen Mohammadzadeh

**Abstract:**

**Keywords:**
ARIMA,
multiple polynomial regression,
production
throughput,
uncertainties

##### 3557 Optimization of Slider Crank Mechanism Using Design of Experiments and Multi-Linear Regression

**Authors:**
Galal Elkobrosy,
Amr M. Abdelrazek,
Bassuny M. Elsouhily,
Mohamed E. Khidr

**Abstract:**

Crank shaft length, connecting rod length, crank angle, engine rpm, cylinder bore, mass of piston and compression ratio are the inputs that can control the performance of the slider crank mechanism and then its efficiency. Several combinations of these seven inputs are used and compared. The throughput engine torque predicted by the simulation is analyzed through two different regression models, with and without interaction terms, developed according to multi-linear regression using LU decomposition to solve system of algebraic equations. These models are validated. A regression model in seven inputs including their interaction terms lowered the polynomial degree from 3^{rd} degree to 1^{st }degree and suggested valid predictions and stable explanations.

**Keywords:**
Design of experiments,
regression analysis,
SI Engine,
statistical modeling.

##### 3556 Non-Methane Hydrocarbons Emission during the Photocopying Process

**Authors:**
Kiurski S. Jelena,
Aksentijević M. Snežana,
Kecić S. Vesna,
Oros B. Ivana

**Abstract:**

**Keywords:**
Indoor air quality,
multiple regression analysis,
nonmethane
hydrocarbons,
photocopying process.

##### 3555 Aircraft Gas Turbine Engines Technical Condition Identification System

**Authors:**
A. M. Pashayev,
C. Ardil,
D. D. Askerov,
R. A. Sadiqov,
P. S. Abdullayev

**Abstract:**

In this paper is shown that the probability-statistic methods application, especially at the early stage of the aviation gas turbine engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence is considered the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic and Neural Networks methods. Training with high accuracy of fuzzy multiple linear and non-linear models (fuzzy regression equations) which received on the statistical fuzzy data basis is made. Thus for GTE technical condition more adequate model making are analysed dynamics of skewness and kurtosis coefficients' changes. Researches of skewness and kurtosis coefficients values- changes show that, distributions of GTE work parameters have fuzzy character. Hence consideration of fuzzy skewness and kurtosis coefficients is expedient. Investigation of the basic characteristics changes- dynamics of GTE work parameters allows to draw conclusion on necessity of the Fuzzy Statistical Analysis at preliminary identification of the engines' technical condition. Researches of correlation coefficients values- changes shows also on their fuzzy character. Therefore for models choice the application of the Fuzzy Correlation Analysis results is offered. For checking of models adequacy is considered the Fuzzy Multiple Correlation Coefficient of Fuzzy Multiple Regression. At the information sufficiency is offered to use recurrent algorithm of aviation GTE technical condition identification (Hard Computing technology is used) on measurements of input and output parameters of the multiple linear and non-linear generalised models at presence of noise measured (the new recursive Least Squares Method (LSM)). The developed GTE condition monitoring system provides stage-bystage estimation of engine technical conditions. As application of the given technique the estimation of the new operating aviation engine temperature condition was made.

**Keywords:**
Gas turbine engines,
neural networks,
fuzzy logic,
fuzzy statistics.

##### 3554 Harmonics Elimination in Multilevel Inverter Using Linear Fuzzy Regression

**Authors:**
A. K. Al-Othman,
H. A. Al-Mekhaizim

**Abstract:**

**Keywords:**
Multilevel converters,
harmonics,
pulse widthmodulation (PWM),
optimal control.