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The prediction of carbon emissions from construction land in central Yunnan urban agglomeration area based on multiple linear regression model

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INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES 2023年第4-5期45卷 349-365页

作者： Niu, Lede Lin, Jingzhi Zhou, Lifang Li, Anlin Zhou, Yan Yunnan Normal Univ Fac Geog Kunming 650500 Peoples R China Yunnan Normal Univ Sch Tourism & Geog Sci Kunming Peoples R China Yunnan Normal Univ Coll Tourism & Geog Sci Kunming Peoples R China

In order to clarify the quantitative relationship between construction land changes and carbon emissions, a prediction method for carbon emissions from construction land in central Yunnan urban agglomeration area based on multiple linear regression model was proposed. Taking the central Yunnan urban agglomeration area as the study area, based on the data of construction land from 2011 to 2020, the carbon emission of construction land was predicted by using the multiple linear regression model. There is a positive correlation between the carbon emissions of the central Yunnan urban agglomeration area and the level of construction land use. From 2011 to 2020, average annual growth rate of construction land area was 8.56%, and the average annual growth rate of carbon emissions was 5.75%. The annual growth rate of carbon emissions from 2021 to 2030 is 0.97%, indicating that the government's carbon emission control measures have achieved good results.

关键词： multiple linear regression model STIRPAT model central Yunnan urban agglomeration area construction land carbon emission prediction

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A Study on Sailboat Price Prediction Method Based on multiple linear regression model and XGBoost Algorithm 6

A Study on Sailboat Price Prediction Method Based on Multipl...

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2023 IEEE 6th International Conference on Information Systems and Computer Aided Education, ICISCAE 2023

作者： Chen, Benteng Beijing University of Chemical Technology School of International Education Beijing China

ISBN: (纸本)9798350313444

this study aims to explore sailboat price prediction methods through the utilization of a multiple linear regression model and the XGBoost algorithm, addressing the pricing challenges in the second-hand sailboat market. The flourishing global economy has led to widespread interest in the second-hand vessel market. This paper provides a dataset containing detailed information on approximately 3,500 sailboats, encompassing attributes such as brand, length, geographic region, and listing price. The model is built upon sound assumptions and constraints, ensuring the reliability of the outcomes. Data preprocessing involves merging, handling outliers, addressing duplicates, and reducing data dimensions. PCA and stepwise regression are employed for dimensionality reduction. The model employs both the multiple linear regression and XGBoost algorithm to establish predictive models. Through a case study involving the Hong Kong market, the model's predictive performance is optimized. Results demonstrate that the model exhibits strong predictive capabilities for specific regional markets. Through the integration of the multiple linear regression model and the XGBoost algorithm, this study presents a valuable sailboat price prediction approach, offering decision support for market practitioners and investors. The research outcomes hold practical applications and academic value in the context of second-hand sailboat transactions. © 2023 IEEE.

关键词： multiple linear regression model PCA Sailboat price prediction method stepwise regression XGBoost algorithm

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Learners' continuance intention in multimodal language learning education: An innovative multiple linear regression model

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HELIYON 2024年第6期10卷 e28104页

作者： Huang, Yan Xu, Wei Sukjairungwattana, Paisan Yu, Zhonggen East China Normal Univ Sch Foreign Languages Shanghai 200241 Peoples R China Zhejiang Yuexiu Univ Sch English Language Shaoxing 312000 Zhejiang Peoples R China City Univ Macau Fac Humanities & Social Sci Ave Padre Tomas Pereira Taipa 999078 Macau Peoples R China Mahidol Univ Fac Liberal Arts 999 Phuttamonthon 4 Rd Salaya 73170 Nakhon Pathom Thailand Beijing Language & Culture Univ Fac Foreign Studies 15 Xueyuan Rd Beijing 100083 Peoples R China

Confronted with the unprecedented COVID-19 pandemic, millions of learners have received, are receiving, or will receive multimodal language learning education. This study aims to explore the relationships between various factors influencing learners' continuance intention by proposing an innovative multiple linear regression model in multimodal language learning education. Participants were randomly recruited (N = 334) in China who had received multimodal language learning education by combining Massive Open Online Courses, Rain Classroom, and WeChat. The research instrument, a comprehensive questionnaire, was sent through the online system named Questionnaire Star developed by technical experts. A multiple linear regression analysis was adopted to test the proposed hypotheses and fit the research model. This study confirms the relationships between the Technology Acceptance model-inclusive constructs such as perceived ease of use, perceived usefulness, attitudes toward multimodal language learning education, and continuance intention of participating in multimodal language learning education. The Technology Acceptance model is also associated with other constructs, e.g. Task-technology fit, Individual-technology fit, Openness, and Reputation of multimodal language learning educational institutes, and personal investment in multimodal language learning education. However, personal investment neither directly nor indirectly predicts continuance intention. Educators and designers could make every effort to improve multimodal language learning education to enhance personal investment and foster its association with continuance intention of learners.

关键词： multiple linear regression model Multimodal language learning education Personal investment Continuance intention

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Construction and Application of multiple linear regression model for Construction Project Cost

Construction and Application of Multiple Linear Regression M...

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International Conference on Advanced Enterprise Information System (AEIS)

作者： Hai, Chen Jinan Univ Gen Logist Management Off Restorat Project Management Sect Guangzhou 510000 Peoples R China

ISBN: (纸本)9781665427241

In order to improve the accuracy of construction project cost budget, based on the previous research, this paper introduces the multiple linear analysis method, constructs a regression model, and uses the model to budget the project cost. The model contains 16 factors. SPSS software is used for weight statistical analysis to extract 4 common factors to improve the structure of the regression model. The reliability of the model is tested by application analysis. The quoted results show that the maximum budget deviation rate is 4.80%, which is within 10%. Therefore, the model can be used as an effective tool for project cost analysis and popularized in the construction field.

关键词： multiple linear regression model Project cost budget

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The Second-Hand House Price Prediction Using multiple linear regression model

The Second-Hand House Price Prediction Using Multiple Linear...

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作者： Yuhan Jin Binghamton University

Analyze the influencing factors of second-hand housing and build the relevant model using the data on second-hand housing prices in *** R-code analysis is used to construct a prediction model of house prices,and the main factors affecting their changes are *** to the significance test,the model meets the expectation and is ***,it is concluded that the most noticeable impact on housing prices is room distribution and ladder ratio,and the least obvious is trade time.

关键词： second-hand house price forecast multiple linear regression model regression analysis

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Development of a multiple linear regression model for Meteorological Drought Index Estimation Based on Landsat Satellite Imagery

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WATER 2020年第12期12卷 3393页

作者： Kim, Seon Woo Jung, Donghwi Choung, Yun-Jae Keimyung Univ Dept Civil Engn 1095 Dalgubeol Daero Daegu 42601 South Korea Korea Univ Sch Civil Environm & Architectural Engn 145 Anam Ro Seoul 02841 South Korea GEO C&I Co Ltd Geospatial Res Ctr 435 Hwarang Ro Daegu 41165 South Korea

Climate polarization due to global warming has increased the intensity of drought in some regions, and the need for drought estimation studies to help minimize damage is increasing. In this study, we constructed remote sensing and climate data for Boryeong, Chungcheongnam-do, Korea, and developed a model for drought index estimation by classifying data characteristics and applying multiple linear regression analysis. The drought indices estimated in this study include four types of standardized precipitation indices (SPI1, SPI3, SPI6, and SPI9) used as meteorological drought indices and calculated through cumulative precipitation. We then applied statistical analysis to the developed model and assessed its ability as a drought index estimation tool using remote sensing data. Our results showed that its adj.R-2 value, achieved using cumulative precipitation for one month, was very low (approximately 0.003), while for the SPI3, SPI6, and SPI9 models, the adj.R-2 values were significantly higher than the other models at 0.67, 0.64, and 0.56, respectively, when the same data were used.

关键词： Landsat remote sensing data drought index SPI multiple linear regression model Boryeong

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Economic modeling of mechanized and semi-mechanized rainfed wheat production systems using multiple linear regression model

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Information Processing in Agriculture 2020年第1期7卷 30-40页

作者： Mobin Amoozad-Khalili Reza Rostamian Mahdi Esmaeilpour-Troujeni Armaghan Kosari-Moghaddam Department of Agricultural Economic Qaemshahr BranchIslamic Azad UniversityIran Department of Biosystems Engineering Ferdowsi University of MashhadMashhadIran Department of Biosystems Engineering University of TabrizTabrizIran

Mathematical modeling of economic indices is a challenging topic in crop production *** present study aimed to model the economic indices of mechanized and semimechanized rainfed wheat production systems using various multiple linear regression *** study area was Behshahr County located in the east of Mazandaran Province,Northern *** statistical population included all wheat producers in Behshahr County in 2016/17 crop *** input variables were human labor,machinery,diesel fuel,chemical(chemical fertilizers and chemical pesticides)costs,and the income was considered to be the *** results showed that the cost of wheat production in the semimechanized system was higher than that of the mechanized *** both systems,the highest cost was related to agricultural machinery ***,seed cost was lower in the mechanized system than that of the semi-mechanized *** net return indicator was 993.68$ha1 and 626.71$ha1 for the mechanized and semi-mechanized systems,*** average benefit to cost ratio was 3.46 and 2.40 for the mechanized and semi-mechanized systems,respectively,demonstrating the greater profitability of the mechanized *** results of the evaluation of five types of regression models including the Cobb-Douglas,linear,2FI,quadratic and pure-quadratic for the mechanized and semi-mechanized production systems indicated that in the developed Cobb-Douglas model,the R2-value was higher than that of the quadratic model while RMSE and MAPE of the quadratic model were determined to be smaller than that of the Cobb-Douglas ***,the best model to investigate the relationship between input costs and the income of wheat production in both mechanized and semi-mechanized systems was the quadratic model.

关键词： Rainfed wheat Economic modeling multiple linear regression model Production costs

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Testing Method of Heteroscedasticity in multiple linear regression model

Frontiers in Economics and Management

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Frontiers in Economics and Management 2021年第6期2卷 67-72页

作者： Fang Cai

The multiple linear regression model is one of the most common models. When the model is applied to economic structure analysis, economic forecasting, policy evaluation and testing, etc., in order to play its role more accurately, it must be ensured that the model does not have heteroscedasticity. Therefore, it is also an important issue to test whether the model has heteroscedasticity. This article mainly sorts out the test methods of multiple linear regression heteroscedasticity and its applicability, in the hope that the multiple linear regression heteroscedasticity test can be selected appropriately.

关键词： multiple linear regression model Heteroscedasticity Testing Method

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Matching Technology of Internet of Things Based on multiple linear regression model in Urban Management

Matching Technology of Internet of Things Based on Multiple ...

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International Conference on Cyber Security Intelligence and Analytics (CSIA)

作者： Feng, Peilu Wang, Pingquan Hohhot Minzu Coll Hohhot 010051 Peoples R China Inner Mongolia Univ Finance & Econ Sch Comp & Informat Management Hohhot 010070 Peoples R China

ISBN: (纸本)9783030152352;9783030152345

The acceleration of urbanization leads to higher standards and requirements for Internet of Things matching technology. Based on this, the matching technology of Internet of things based on multiple linear regression model in urban management is put forward. Data in the Internet of Things are processed accurately. Wireless sensors with optimal functions are selected, and mathematical models are established and solved. Through experimental demonstration, it is concluded that the Internet of Things matching technology based on multiple linear regression model greatly reduces the cost of urban management.

关键词： City management multiple linear regression model Internet of Things Matching technology

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Research on Impact of Environmental Information Disclosure on Corporate Performance Based on multiple linear regression Computation model 5

Research on Impact of Environmental Information Disclosure o...

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5th IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2023

作者： Zheng, Siya Wuhan University of Science and Technology School of Management Wuhan China

ISBN: (纸本)9798350314694

With the continuous development of the social economy, the pollution of the natural environment has become increasingly serious. The purpose of this study was to explore the impact of environmental information disclosure on corporate performance to strengthen enterprises' attention to pollution prevention and control, and the construction of environmental accounting information disclosure. A case study and empirical analysis methods were used to construct multiple linear regression equations taking ten listed companies with similar sizes from 2017 to 2021 as samples. The impact of environmental information disclosure on corporate performance was evaluated. At the same time, the Jingxing Paper industry was analyzed how environmental accounting information disclosure affected enterprise performance in the heavily polluted paper industry to promote the green development of enterprises and the sustainable development of China's economy. The results showed that the level of environmental accounting information disclosure has a positive promoting effect on corporate performance. For development, enterprises pay attention to the disclosure of environmental information to achieve a win-win situation of environmental benefits and economic benefits. © 2023 IEEE.

关键词： enterprise performance environmental information disclosure environmental pollution multiple linear regression model paper industry

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