scholarly journals Real Estate Values and Urban Quality: A Multiple Linear Regression Model for Defining an Urban Quality Index

2021 ◽  
Vol 13 (24) ◽  
pp. 13635
Author(s):  
Sebastiano Carbonara ◽  
Marco Faustoferri ◽  
Davide Stefano
Keyword(s):  
Linear Regression ◽  
Real Estate ◽  
Linear Regression Model ◽  
Quality Index ◽  
Property Values ◽  
Multiple Linear Regression Model ◽  
Urban Environments ◽  
Urban Quality ◽  
The City

Urban quality, real estate values and property taxation are different factors that participate in defining how a city is governed. Real estate values are largely determined by the characteristics of urban environments in which properties are located and, thus, by quality of the location. Beginning with these considerations, this paper explores the theme of urban quality through a study of property values that seeks to define all physical (and thus measurable) characteristics that participate in defining urban quality. For this purpose, a multiple linear regression model was developed for reading the residential real estate market in the city of Pescara (Italy). In addition to the intrinsic characteristics of a property (floor area, period of construction/renovation, level, building typology and presence of a garage), input also included extrinsic data represented by the Urban Quality Index. Scientific literature on this theme tells us that many independent variables influence real estate prices, although all are linked to a set of intrinsic characteristics (property-specific) and to a set of extrinsic characteristics (specific to the urban context in which the property is located) and, thus, to the quality of urban environments. The index developed was produced by the analytical and simultaneous reading of four macrosystems with the greatest impact on urban quality: environment, infrastructure, settlement and services (each with its own subsystems). The results obtained made it possible to redefine proportional ratios between various parts of the city of Pescara, based on a specific Urban Quality Index, and to recalculate market property values used to calculate taxes in an attempt to resolve the inequality that persists in this field.

scholarly journals ANALISIS FAKTOR - FAKTOR YANG MEMPENGARUHI HARGA TANAH UNTUK PENGGUNAAN PERUMAHAN (STUDI KASUS: KECAMATAN BANYUMANIK)

2017 ◽  
Vol 5 (1) ◽  
pp. 57
Author(s):  
Triana Kurniwati ◽  
Bagio Mudakir
Keyword(s):  
Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Least Square Method ◽  
Research Area ◽  
Least Square ◽  
City Center ◽  
Land Price ◽  
Ordinary Least Square ◽  
The City

Semarang city is densely populated that demand of settlement will increase continually, but land in city center is very limited and even it is scarce, therefore the land price which is placed in city center is high. That is why many inhabitant of Semarang city prefer to live in outskirts of the city. The shifting of land demand to the outskirts is also followed by increasing of land price in outskirts, it causes the land price in outskirts is uncontrolled.The research takes location in Banyumanik area. This research area consists of 7 districts, that are Jabungan, Pudak Payung, Banyumanik, Srondol Kulon, Pedalangan, Ngesrep, and Gedawang district. The sample total is one hundred (100). The data is analyzed by using multiple linear regression model with ordinary least square method (OLS).

scholarly journals Development of Models for Children—Pedestrian Crossing Speed at Signalized Crosswalks

2021 ◽  
Vol 13 (2) ◽  
pp. 777
Author(s):  
Irena Ištoka Otković ◽  
Aleksandra Deluka-Tibljaš ◽  
Sanja Šurdonja ◽  
Tiziana Campisi
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Network Model ◽  
Neural Network Model ◽  
Multiple Linear Regression Model ◽  
Urban Environments ◽  
The Neural Network ◽  
The City ◽  
Traffic Behavior

Modeling the behavior of pedestrians is an important tool in the analysis of their behavior and consequently ensuring the safety of pedestrian traffic. Children pedestrians show specific traffic behavior which is related to cognitive development, and the parameters that affect their traffic behavior are very different. The aim of this paper is to develop a model of the children-pedestrian’s speed at a signalized pedestrian crosswalk. For the same set of data collected in the city of Osijek—Croatia, two models were developed based on neural network and multiple linear regression. In both cases the models are based on 300 data of measured children speed at signalized pedestrian crosswalks on primary city roads located near a primary school. As parameters, both models include the selected traffic infrastructure features and children’s characteristics and their movements. The models are validated on data collected on the same type of pedestrian crosswalks, using the same methodology in two other urban environments—the city of Rijeka, Croatia and Enna in Italy. It was shown that the neural network model, developed for Osijek, can be applied with sufficient reliability to the other two cities, while the multiple linear regression model is applicable with relatively satisfactory reliability only in Rijeka. A comparative analysis of the statistical indicators of reliability of these two models showed that better results are achieved by the neural network model.

scholarly journals Selection of Appropriate Model for Quality of Life Data of Strabismus Patients Despite Censorship

2021 ◽  
Author(s):  
Zahra Ghassemi ◽  
Mehdi Yaseri ◽  
Mostafa Hosseini
Keyword(s):  
Quality Of Life ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Tobit Model ◽  
P Value ◽  
Response Variable

Introduction: Previous studies on the quality of life of strabismus patients have not examined the existence of censoring to express the relation between the response variable and its predictors. Methods & Materials: The information used in this study is a conducted cross-sectional study in 2012. The sample size is 90 children in the age range (4-18) years and with congenital strabismus. We used the RAND Health Insurance Study questionnaire with ten subscales to evaluate the quality of life, which was increased to 11 dimensions by adding some items related to eye alignment concerns introduced by Archer et al. The demographic profile is also recorded by 13 other questions. We have expressed the relationship between the independent and response variables in each of the 11 dimensions of the questionnaire and the overall quality of life score by fitting the multiple linear regression model. Then we fitted the two models of classic Tobit and CLAD, which are for censoring, to all dimensions of the questionnaire. Results: We showed that in fitting the models to the overall quality of life scale variable, the best model is the multiple linear regression. Because the response variable was normal, and there was no censoring (ceiling and floor effect). However, in the depression subscale, due to the high censoring (28.89% of the ceiling effect) and the almost normal distribution of the response variable (p-value of skewness< 0.05), the appropriate model according to the criteria is the classic Tobit (AIC = 546.33). That is, the classic Tobit model is the best alternative to the multiple linear regression model in the presence of censoring. But these conditions did not exist in all variables. In the subscale, there was a severe censoring performance constraint (67.78% of the ceiling effect). When censoring is high, the distribution of the response variable becomes very skewed, and the distribution of response variables deviates drastically from normal. The distribution of the performance constraint variable was very skewed (p-value <0.001). Here the RMSE standard scale for the classic Tobit model was 28.74, which is much higher than the standard scale for the multiple linear regression model (14.23). The best model for the high censoring was CLAD. Conclusion: To use the appropriate statistical method in the analysis, one must look at how the response variable is distributed. The multiple linear regression model is very widely used, but in the presence of censoring, the use of this model gives skewed results. In this case, the classic Tobit model and its derived model, CLAD, are replaced. The nonparametric CLAD model calculates accurate estimates with minimum defaults and censoring.

scholarly journals Finite-Difference Models Application for Short-Term Forecasting of the Natural Resource Potential of the Perm Region

2021 ◽  
Vol 21 (2) ◽  
pp. 154-166
Author(s):  
N.A. Sirotina ◽  
◽  
A.V. Kopoteva ◽  
A.V. Zatonskiy
Keyword(s):  
Linear Regression ◽  
Finite Difference ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Natural Resource ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Resource Potential ◽  
Predicted Values

The article is about a problem of mathematical modeling of the natural resource potential of the Perm Territory by 1st and 2nd order finite-difference models. Such models can obtain better forecasts of complex socio-economic processes in comparison with the traditionally used linear multiple regression models. A high quality model of the natural resource potential with forecast possibi¬lities is one of the necessary conditions for the effective management of the natural resources of the region in order to ensure its sustainable economic development. Purpose of work. Aim of this work is work construction of finite-difference models of a natural resource potential complex indicators and an assessment of their prognostic properties. Materials and methods. Our research is based on Perm region statistical data for the period from 2001 to 2018. A multiple linear regression model is used as a comparison base. The natural resource potential complex indicator is calculated as a weighted sum of particular criteria characterizing the natural resources of the region. First and second order finite difference models are obtained by adding autoregressive terms of the first and second orders, respectively, to the multiple linear regression model. An estimation of the unknown parameters of the equations is carried out by a modified least squares method, which preserves the signs of the coefficients with the factors the same as in the original linear model. At the same time, the selection of explanatory factors and the assessment of the quality of the models are carried out based on the accuracy of the predicted values of the studied indicator. The results of the study. Components and factors of the natural resource potential is obtained, and a procedure for constructing finite-difference models is performed for three different time intervals: 2001–2018, 2001–2008, and 2008–2018. These intervals are chooseen because changes in the methodology for generating statistical data nearly 2008. Discussion and conclusions. The number of calculated predicted values was 18, and only in 4 out of 18 cases (22,2%) their quality is worse than forecasts obtained by the linear multiple model. So proposed modification of the multiple linear regression model with the addition of autoregressive terms makes it possible to improve the forecasting quality of the complex indicator of the natural resource potential of the region and, therefore, to make more effective decisions when managing its level.

scholarly journals Determinan Kinerja Anggaran pada Organisasi Perangkat Daerah

Akuntabilitas ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 109-124
Author(s):  
Syukriy Abdullah ◽  
Afrah Junita
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Linear Regression Model ◽  
Financial Management ◽  
Multiple Linear Regression Model ◽  
Fiscal Year ◽  
Government Organizations ◽  
Negative Effect ◽  
Work Units

Budget performance is the basis for evaluating and assessing the quality of financial management and budgeting in government organizations, especially in local government’s agencies. The purpose of this study is to examine the effect of budget size (TB), budget change (PB), and previous year budget variance (VS) on budget performance (KA) at agencies of Gayo Lues Regency. The sample in this study amounted to 44 work units of regional apparatus (SKPD) for fiscal year 2016-2017. Data analysis using multiple linear regression model. The results show that PB has no effect on KA, while TB and VS have negative effect on KA

A fuzzy multiple linear regression model based on meteorological factors for air quality index forecast

2021 ◽  
pp. 1-25
Author(s):  
Yujie Gu ◽  
Yuxiu Zhao ◽  
Jian Zhou ◽  
Hui Li ◽  
Yujie Wang
Keyword(s):  
Air Quality ◽  
Linear Regression ◽  
Linear Regression Model ◽  
Regression Models ◽  
Quality Index ◽  
Fuzzy Numbers ◽  
Meteorological Factors ◽  
Multiple Linear Regression Model ◽  
Linear Regression Models ◽  
Fuzzy Linear Regression

Air quality index (AQI) is an indicator usually issued on a daily basis to inform the public how good or bad air quality recently is or how it will become over the next few days, which is of utmost importance in our life. To provide a more practicable way for AQI prediction, so that residents can clear about air conditions and make further plans, five imperative meteorological indicators are elaborately selected. Accordingly, taking these indicators as independent variables, a fuzzy multiple linear regression model with Gaussian fuzzy coefficients is proposed and reformulated, based on the linearity of Gaussian fuzzy numbers and Tanaka’s minimum fuzziness criterion. Subsequently, historical data in Shanghai from March 2016 to February 2018 are extracted from the government database and divided into two parts, where the first half is statistically analyzed and used for formulating four seasonal fuzzy linear regression models in views of the special climate environment of Shanghai, and the second half is used for prediction to validate the performance of the proposed model. Furthermore, considering that there is beyond dispute that triangular fuzzy number is more prevalent and crucial in the field of fuzzy studies for years, plenty of comparisons between the models based on the two types of fuzzy numbers are carried out by means of the three measures including the membership degree, the fuzziness and the credibility. The results demonstrate the powerful effectiveness and efficiency of the fuzzy linear regression models for AQI prediction, and the superiority of Gaussian fuzzy numbers over triangular fuzzy numbers in presenting the relationships between the meteorological factors and AQI.

scholarly journals Pengaruh Pengetahuan Produk dan Persepsi Kualitas terhadap Niat Beli Mobil Datsun Go Panca

2018 ◽  
Vol 9 (1) ◽  
pp. 26
Author(s):  
Aditya Rian Firmansyah
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Sampling Technique ◽  
Purchase Intentions ◽  
Purchase Intent ◽  
Product Knowledge ◽  
Measuring Instrument

This study aims to investigate and discuss the influence of knowledge and perception of the quality of the product purchase intentions Datsun Go Panca.The sampling technique used is non - probability sampling and sampling conducted by judgmental sampling with a sample taken as many as 210 people. The target populationis the respondents whowere visitinga car show LCGC especially Datsun Go Panca.The scale of measurementin this study using a Likert scale. Measuring instrument usedwas a questionnaire. Analysis of data using multiple linear regression model. The results showedthat the effectof product knowledge on the intentionto buy Datsun Go Panca and influence the quality perception and purchase intent Datsun GoPanca, contributed 49.5%.

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