Correlation Analysis of Fiscal Revenue and Housing Sales Price Based on Multiple Linear Regression Model

This paper selects seven indicators of financial revenue and housing sales price in recent 19 years in China,and uses SPSS and Excel to carry out descriptive statistics,independent sample t-test,correlation analysis and regression analysis to comprehensively study the correlation between financial revenue and housing sales price in China,and establishes the relationship between financial revenue and housing sales price When the average selling price of commercial housing increases by one unit,the fiscal revenue will increase by 27.855 points.

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

Mathematical modeling of economic indices is a challenging topic in crop production systems.The present study aimed to model the economic indices of mechanized and semimechanized rainfed wheat production systems using various multiple linear regression models.The study area was Behshahr County located in the east of Mazandaran Province,Northern Iran.The statistical population included all wheat producers in Behshahr County in 2016/17 crop year.Five input variables were human labor,machinery,diesel fuel,chemical(chemical fertilizers and chemical pesticides)costs,and the income was considered to be the output.The results showed that the cost of wheat production in the semimechanized system was higher than that of the mechanized system.In both systems,the highest cost was related to agricultural machinery input.Moreover,seed cost was lower in the mechanized system than that of the semi-mechanized system.The net return indicator was 993.68$ha1 and 626.71$ha1 for the mechanized and semi-mechanized systems,respectively.The 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 system.The 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 model.Therefore,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.

Multiple linear regression models of urban runoff pollutant load and event mean concentration considering rainfall variables

Rainfall is an important factor in estimating the event mean concentration （EMC） which is used to quantify the washed-off pollutant concentrations from non-point sources （NPSs）. Pollutant loads could also be calculated using rainfall, catchment area and runoff coefficient. In this study, runoff quantity and quality data gathered from a 28-month monitoring conducted on the road and parking lot sites in Korea were evaluated using multiple linear regression （MLR） to develop equations for estimating pollutant loads and EMCs as a function of rainfall variables. The results revealed that total event rainfall and average rainfall intensity are possible predictors of pollutant loads. Overall, the models are indicators of the high uncertainties of NPSs; perhaps estimation of EMCs and loads could be accurately obtained by means of water quality sampling or a long term monitoring is needed to gather more data that can be used for the development of estimation models.

Analyzing geomorphological and topographical controls for the heterogeneous glacier mass balance in the Sikkim Himalayas

Glacier response patterns at the catchment scale are highly heterogeneous and defined by a complex interplay of various dynamics and surface factors.Previous studies have explained heterogeneous responses in qualitative ways but quantitative assessment is lacking yet where an intrazone homogeneous climate assumption can be valid.Hence,in the current study,the reason for heterogeneous mass balance has been explained in quantitative methods using a multiple linear regression model in the Sikkim Himalayan region.At first,the topographical parameters are selected from previously published studies,then the most significant topographical and geomorphological parameters are selected with backward stepwise subset selection methods.Finally,the contributions of selected parameters are calculated by least square methods.The results show that,the magnitude of mass balance lies between-0.003±0.24 to-1.029±0.24 m.w.e.a^(-1) between 2000 and 2020 in the Sikkim Himalaya region.Also,the study shows that,out of the terminus type of the glacier,glacier area,debris cover,ice-mixed debris,slope,aspect,mean elevation,and snout elevation of the glaciers,only the terminus type and mean elevation of the glacier are significantly altering the glacier mass balance in the Sikkim Himalayan region.Mathematically,the mass loss is approximately 0.40 m.w.e.a^(-1) higher in the lake-terminating glaciers compared to the land-terminating glaciers in the same elevation zone.On the other hand,a thousand meters mean elevation drop is associated with 0.179 m.w.e.a-1of mass loss despite the terminus type of the glaciers.In the current study,the model using the terminus type of the glaciers and the mean elevation of the glaciers explains 76% of fluctuation of mass balance in the Sikkim Himalayan region.

Regression analysis and its application to oil and gas exploration:A case study of hydrocarbon loss recovery and porosity prediction,China

In oil and gas exploration,elucidating the complex interdependencies among geological variables is paramount.Our study introduces the application of sophisticated regression analysis method at the forefront,aiming not just at predicting geophysical logging curve values but also innovatively mitigate hydrocarbon depletion observed in geochemical logging.Through a rigorous assessment,we explore the efficacy of eight regression models,bifurcated into linear and nonlinear groups,to accommodate the multifaceted nature of geological datasets.Our linear model suite encompasses the Standard Equation,Ridge Regression,Least Absolute Shrinkage and Selection Operator,and Elastic Net,each presenting distinct advantages.The Standard Equation serves as a foundational benchmark,whereas Ridge Regression implements penalty terms to counteract overfitting,thus bolstering model robustness in the presence of multicollinearity.The Least Absolute Shrinkage and Selection Operator for variable selection functions to streamline models,enhancing their interpretability,while Elastic Net amalgamates the merits of Ridge Regression and Least Absolute Shrinkage and Selection Operator,offering a harmonized solution to model complexity and comprehensibility.On the nonlinear front,Gradient Descent,Kernel Ridge Regression,Support Vector Regression,and Piecewise Function-Fitting methods introduce innovative approaches.Gradient Descent assures computational efficiency in optimizing solutions,Kernel Ridge Regression leverages the kernel trick to navigate nonlinear patterns,and Support Vector Regression is proficient in forecasting extremities,pivotal for exploration risk assessment.The Piecewise Function-Fitting approach,tailored for geological data,facilitates adaptable modeling of variable interrelations,accommodating abrupt data trend shifts.Our analysis identifies Ridge Regression,particularly when augmented by Piecewise Function-Fitting,as superior in recouping hydrocarbon losses,and underscoring its utility in resource quantification refinement.Meanwhile,Kernel Ridge Regression emerges as a noteworthy strategy in ameliorating porosity-logging curve prediction for well A,evidencing its aptness for intricate geological structures.This research attests to the scientific ascendancy and broad-spectrum relevance of these regression techniques over conventional methods while heralding new horizons for their deployment in the oil and gas sector.The insights garnered from these advanced modeling strategies are set to transform geological and engineering practices in hydrocarbon prediction,evaluation,and recovery.

Prediction Model of Secondary Substances in Anthocyanins Synthesis of Purple Corn

The aim of this study was to assay the polyphenols,flavonoid,polyphenol oxidase and phenylalnine ammonialyase which were relative to the anthocyanins synthesis of purple corn. The optimization of multiple linear regression model of anthocyanins synthesis was y=4.383 86-0.205 45x1＋5.479 638x2＋0.195 575x4. According to standard partial regression coefficient testing,the result indicated that polyphenols content was negatively correlated with anthocyanins and the relative influence to anthocyanins synthesis was-42.7%; flavonoid content and activity of polyphenol oxidase were positively correlated with anthocyanins of purple corn and the relative influence to anthocyanins synthesis were 71.45% and 73.32% respectively. There was no positive correlation between the activity of phenylalnine ammonialyase and anthocyanins of purple corn. The establishment of multiple linear regression model of anthocyanins synthesis was to provide theory foundation of producing anthocyanins in laboratory.

Dew amount and its long-term variation in the Kunes River Valley,Northwest China

Dew is an essential water resource for the survival and reproduction of organisms in arid and semi-arid regions.Yet estimating the dew amount and quantifying its long-term variation are challenging.In this study,we elucidate the dew amount and its long-term variation in the Kunes River Valley,Northwest China,based on the measured daily dew amount and reconstructed values(using meteorological data from 1980 to 2021),respectively.Four key results were found:(1)the daily mean dew amount was 0.05 mm during the observation period(4 July-12 August and 13 September-7 October of 2021).In 35 d of the observation period(i.e.,73%of the observation period),the daily dew amount exceeded the threshold(>0.03 mm/d)for microorganisms;(2)air temperature,relative humidity,and wind speed had significant impacts on the daily dew amount based on the relationships between the measured dew amount and meteorological variables;(3)for estimating the daily dew amount,random forest(RF)model outperformed multiple linear regression(MLR)model given its larger R^(2) and lower MAE and RMSE;and(4)the dew amount during June-October and in each month did not vary significantly from 1980 to the beginning of the 21^(st) century.It then significantly decreased for about a decade,after it increased slightly from 2013 to 2021.For the whole meteorological period of 1980-2021,the dew amount decreased significantly during June-October and in July and September,and there was no significant variation in June,August,and October.Variation in the dew amount in the Kunes River Valley was mainly driven by relative humidity.This study illustrates that RF model can be used to reconstruct long-term variation in the dew amount,which provides valuable information for us to better understand the dew amount and its relationship with climate change.

Parker Test for Heteroskedasticity Based on Sample Fitted Values

To address the drawbacks of the traditional Parker test in multivariate linear models: the process is cumbersome and computationally intensive, we propose a new heteroscedasticity test. A new heteroskedasticity test is proposed using the fitted values of the samples as new explanatory variables, reconstructing the regression model, and giving a new heteroskedasticity test based on the significance test of the coefficients, it is also compared with the existing Parker test which is improved using the principal component idea. Numerical simulations and empirical analyses show that the improved Parker test with the fitted values of the samples proposed in this paper is superior.

Updating Methods for Real Time Flood Forecasting： A Comparison through Senegal River Basin Upstream Bakel

Heavy floods occur frequently in the Senegal River Basin, causing catastrophic flooding downstream the river rating station of Bakel. Anticipating the occurrence of such phenomena is the only way to reduce the resulting damages. Flood forecasting is a necessity. Flood forecasting plays also an important role in the implementation of flood management scenarios and in the protection of hydro electric structures. Many methods are applied. The most complete are based on the conservation laws of physics governing the free surface flow. These methods need a complete description of the geometry of the river and their implementation requires also huge investments. In practice the river basin can be considered as a system of inputs-outputs related by a transfer function. In this paper the authors first used a multiple linear regression model with constant parameters estimated by the ordinary least square method to simulate the propagation of the floods in the upstream part of the Senegal river basin. The authors then apply statistical and graphical criteria of goodness-of-fit to test the suitability of this model. Three procedures of parameters updating have then been added to this linear model： the Kalman filter method, the recursive least square method, and the stochastic gradient method The criteria of goodness-of-fit used above have shown that the stochastic gradient method, although more rudimentary, represents better the flood propagation in the head basin of the Senegal river upstream Bakel. This result is particularly interesting because data influenced by Manantali Dam are used.

Economic Consequences of Gender Equality in Europe

This paper shows influence of gender equality on economy where it analyzed how gender equality in Europe has affected on the development of the frozen food industry and services related to childcare. The development of these industries has given a positive impulse to the development of the whole economy. In this analysis, it is used multiple regressions as one of the most important statistical methods. In the first part of this paper, it shows the connection among the growth of female employment, growth in frozen food expenditure and growth of GDP in United Kingdom. In the second part of paper, it shows the relationship among the growth of labor force participation of women, growth of number of kindergarten and growth of GDP in Hungary. To proof these relationships, it used a multiple regression model. This statistical model was tested by using the T schedule which showed that the model in both the analyses is correct. At the end of the paper, it presents that employment rate and GDP behaves in the same way in European Union. These analyses show that it is necessary to continue to strengthen gender equality if the policy makers want to achieve even greater economic growth. The issue of gender equality is a very important factor in creating employment policy, and statisticians should be more involved in process of employment policy and gender equality

Analysis of the Relationship Between Railway and Highway Transportation and China's Economic Development

China has a vast land area and frequent interconnections between various regions.China's transportation industry is faced with tremendous pressure.This article combines China’s railway and highway transportation conditions to predict China’s economic development,uses stepwise regression to screen explanatory variables,and finally determines railway passenger turnover,road freight volume and passenger car ownership as the explanatory variables,and GDP as the dependent variable,and also analyzes China’s economic development by establish ing a multiple regression model.

Parameter Optimization via Orthogonal Experiment to Improve Accuracy of Metakaolin Ceramics Fabricated by Direct Ink Writing

Kaolin/metakaolin-insulating ceramic components fabricated using direct ink writing(DIW)have important ap-plication prospects in architecture and aerospace.The accuracy of the entire process including the forming and sintering accuracy of ceramics greatly limits the application scope,and high-accuracy ceramic samples can meet the usage requirements in many scenarios.The orthogonal experiment was designed with four process parame-ters,including nozzle internal diameter,filling rate,printing layer height/nozzle internal diameter,and printing speed,to investigate the evolution of the DIW forming accuracy,sintering shrinkage rate and surface roughness of metakaolin-based ceramics with different process parameters.The influence of each process parameter and its mechanism were analyzed to obtain the DIW parameters for high-accuracy metakaolin ceramics.Multiple linear regression models between the dimensional change rate,surface roughness,and process parameters of the ceramic samples were established and validated.The results show that comprehensively considering the forming accuracy of the ceramic green bodies,sintering shrinkage rate and surface roughness,the optimal DIW process parameters were a 0.41 mm nozzle internal diameter,100%filling rate,50%printing layer height/nozzle inter-nal diameter,and a 15 mm/s printing speed.Multiple linear regression models were developed for the process parameters and the printing accuracy,sintering shrinkage rate and surface roughness.The error rates between the theoretical results obtained by substituting the optimal process parameters into the multiple linear regression models and the actual results obtained by printing the samples with the optimal parameters were extremely small,all less than 0.8%.This verified the correctness and predictability of the multiple linear regression models.This work provides a reference basis for rapid fabrication of high-accuracy ceramics via DIW and accuracy prediction with different process parameters.

Nitrous Oxide Emissions from a Masson Pine Forest Soil in Subtropical Central China

The forest ecosystem plays a pivotal role in contributing greenhouse gases to the atmosphere.In order to characterize the temporal pattern of nitrous oxide(N_2O) emissions and identify the key factors affecting N_2O emissions from a Masson pine forest in a hilly red-soil region in subtropical central China,we measured the N_2O emissions in Jinjing of Hunan Province using the static chambergas chromatographic method for 3 years(2010-2012) and analyzed the relationships between the N_2O fluxes and the environmental variables.Our results revealed that the N_2O fluxes over the 3 years varied from-36.0 to 296.7 μg N m^(-2) h^(-1),averaging 18.4±5.6 μg N m^(-2) h^(-1)(n=3).The average annual N_2O emissions were estimated to be 1.6±0.3 kg N ha^(-1) year^(-1).The N_2O fluxes exhibited clear intra-annual(seasonal) variations as they were higher in summers and lower in winters.Compared with other forest observations in the subtropics,N_2O emissions at our site were relatively high,possibly due to the high local dry/wet N deposition,and were mostly sensitive to variations in precipitation and soil ammonium N content.In this work,a multiple linear regression model was developed to determine the influence of environmental factors on N_2O emissions,in which a category predictor of "Season" was intentionally used to account for the seasonal variation of the N_2O fluxes.Such a model explained almost 40%of the total variation in daily N_2O emissions from the Masson pine forest soil studied(P<0.001).

Prediction of Extractable Cd,Pb and Zn in Contaminated Woody Habitat Soils Using a Change Point Detection Method

Accumulation of heavy metals in soils poses a potential risk to plant production, which is related to availability of the metals in soil. The phytoavailability of metals is usually evaluated using extracting solutions such as salts, acids or chelates. The purpose of this study was to identify the most significant soil parameters that can be used to predict the concentrations of acetic and citric acidextractable cadmium(Cd), lead(Pb) and zinc(Zn) in contaminated woody habitat topsoils. Multiple linear regression models were established using two analysis strategies and three sets of variables based on a dataset of 260 soil samples. The performance of these models was evaluated using statistical parameters. Cation exchange capacity, CaCO_3, organic matter, assimilated P, free Al oxide,sand and the total metal concentrations appeared to be the main soil parameters governing the solubility of Cd, Pb and Zn in acetic and citric acid solutions. The results strongly suggest that the metal solubility in extracting solutions is extractable concentrationdependent since models were overall improved by incorporating a change point. This change point detection method was a powerful tool for predicting extractable Cd, Pb and Zn. Suitable predictions of extractable Cd, Pb and Zn concentrations were obtained, with correlation coefficient(adjusted r) ranging from 0.80 to 0.99, given the high complexity of the woody habitat soils studied. Therefore,the predictive models can constitute a decision-making support tool for managing phytoremediation of contaminated soils, making recommendations to control the potential bioavailability of metals. The relationships between acetic and/or citric acid-extractable concentrations and the concentrations of metals into the aboveground parts of plants need to be predicted, in order to make their temporal monitoring easier.

Regional Temperature Forecast for the Next Day in Hong Kong

For a century or so, the Hong Kong Observatory （HKO） has been providing temperature forecast for the whole of Hong Kong with the HKO Headquarters as the reference location. In recent decades, due to spreading of population from the main urban center to satellite towns, there is an increasing demand for regional temperature forecasts. To support such provision, the HKO has developed a regression model to provide objective guidance to forecasters in formulating forecasts of maximum and minimum temperatures for the next day at various locations in Hong Kong. In this paper, the regression model is presented, together with the assessment of its performance. Based on the verification of one year of forecasts, it is found that the root mean square errors （RMSEs） of maximum （minimum） temperature forecasts are from about 1.3 to 2.1 （1.1 to 1.4） degrees, respectively. The regression model is shown to have generally out-performed the operational regional spectral model then operated by HKO. Regional temperature forecast methods of other meteorological or research centers are also surveyed. Equipped with the regression model, the HKO has launched an online regional temperature forecast service for the next day in Hong Kong since March 2008.