**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**9734

# Search results for: multiple linear regression model

##### 9734 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.

##### 9733 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.

##### 9732 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

##### 9731 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.

##### 9730 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.

##### 9729 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.

##### 9728 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.

##### 9727 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.

##### 9726 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.

##### 9725 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.

##### 9724 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.

##### 9723 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.

##### 9722 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

##### 9721 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.

##### 9720 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.

##### 9719 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.

##### 9718 Empirical Statistical Modeling of Rainfall Prediction over Myanmar

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

**Abstract:**

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

##### 9717 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.

##### 9716 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.

##### 9715 Economic Dispatch Fuzzy Linear Regression and Optimization

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

**Abstract:**

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

##### 9714 Multi-Rate Exact Discretization based on Diagonalization of a Linear System - A Multiple-Real-Eigenvalue Case

**Authors:**
T. Sakamoto,
N. Hori

**Abstract:**

**Keywords:**
Multi-rate discretization,
linear systems,
triangularization,
similarity transformation,
diagonalization,
exponential transformation,
multiple eigenvalues

##### 9713 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.

##### 9712 Speaker Independent Quranic Recognizer Basedon Maximum Likelihood Linear Regression

**Authors:**
Ehab Mourtaga,
Ahmad Sharieh,
Mousa Abdallah

**Abstract:**

**Keywords:**
Hidden Markov Model (HMM),
MaximumLikelihood Linear Regression (MLLR),
Quran,
Regression ClassTree,
Speech Recognition,
Speaker-independent.

##### 9711 Multiple Regression based Graphical Modeling for Images

**Authors:**
Pavan S.,
Sridhar G.,
Sridhar V.

**Abstract:**

Super resolution is one of the commonly referred inference problems in computer vision. In the case of images, this problem is generally addressed using a graphical model framework wherein each node represents a portion of the image and the edges between the nodes represent the statistical dependencies. However, the large dimensionality of images along with the large number of possible states for a node makes the inference problem computationally intractable. In this paper, we propose a representation wherein each node can be represented as acombination of multiple regression functions. The proposed approach achieves a tradeoff between the computational complexity and inference accuracy by varying the number of regression functions for a node.

**Keywords:**
Belief propagation,
Graphical model,
Regression,
Super resolution.

##### 9710 Study on Optimal Control Strategy of PM2.5 in Wuhan, China

**Authors:**
Qiuling Xie,
Shanliang Zhu,
Zongdi Sun

**Abstract:**

In this paper, we analyzed the correlation relationship among PM2.5 from other five Air Quality Indices (AQIs) based on the grey relational degree, and built a multivariate nonlinear regression equation model of PM2.5 and the five monitoring indexes. For the optimal control problem of PM2.5, we took the partial large Cauchy distribution of membership equation as satisfaction function. We established a nonlinear programming model with the goal of maximum performance to price ratio. And the optimal control scheme is given.

**Keywords:**
Grey relational degree,
multiple linear regression,
membership function,
nonlinear programming.

##### 9709 The Profit Trend of Cosmetics Products Using Bootstrap Edgeworth Approximation

**Authors:**
Edlira Donefski,
Lorenc Ekonomi,
Tina Donefski

**Abstract:**

**Keywords:**
Bootstrap,
Edgeworth approximation,
independent and Identical distributed,
quantile.

##### 9708 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.

##### 9707 Selection of Designs in Ordinal Regression Models under Linear Predictor Misspecification

**Authors:**
Ishapathik Das

**Abstract:**

**Keywords:**
Model misspecification,
multivariate kriging,
multivariate logistic link,
ordinal response models,
quantile
dispersion graphs.

##### 9706 Detecting Earnings Management via Statistical and Neural Network Techniques

**Authors:**
Mohammad Namazi,
Mohammad Sadeghzadeh Maharluie

**Abstract:**

**Keywords:**
Earnings management,
generalized regression neural
networks,
linear regression,
multi-layer perceptron,
Tehran stock
exchange.

##### 9705 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.